{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:05:17.274767Z",
     "start_time": "2025-06-12T08:04:38.113474Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "def read_files_from_folder(folder_path):\n",
    "    \"\"\"\n",
    "    读取指定文件夹中的所有文件内容\n",
    "    \"\"\"\n",
    "    all_files = []\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith('.txt'):  # 确保只读取文本文件\n",
    "                file_path = os.path.join(root, file)\n",
    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
    "                    all_files.append(f.read())\n",
    "    return all_files\n",
    "\n",
    "# 设置文件夹路径\n",
    "neg_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\neg'\n",
    "pos_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\pos'\n",
    "\n",
    "# 读取文件夹中的所有文件\n",
    "neg_files = read_files_from_folder(neg_folder_path)\n",
    "pos_files = read_files_from_folder(pos_folder_path)\n",
    "\n",
    "# 打印读取的文件数量\n",
    "print(f\"负面评论文件数量：{len(neg_files)}\")\n",
    "print(f\"正面评论文件数量：{len(pos_files)}\")"
   ],
   "id": "2839f8649c2dbbd2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "负面评论文件数量：12500\n",
      "正面评论文件数量：12500\n"
     ]
    }
   ],
   "execution_count": 2
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:13:26.392518Z",
     "start_time": "2025-06-12T08:13:12.616926Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "def read_files_from_folder(folder_path):\n",
    "    \"\"\"\n",
    "    读取指定文件夹中的所有文件内容\n",
    "    \"\"\"\n",
    "    all_files = []\n",
    "    for root, dirs, files in os.walk(folder_path):\n",
    "        for file in files:\n",
    "            if file.endswith('.txt'):  # 确保只读取文本文件\n",
    "                file_path = os.path.join(root, file)\n",
    "                with open(file_path, 'r', encoding='utf-8') as f:\n",
    "                    all_files.append(f.read())\n",
    "    return all_files\n",
    "\n",
    "def write_to_file(file_path, content):\n",
    "    \"\"\"\n",
    "    将内容写入文件\n",
    "    \"\"\"\n",
    "    with open(file_path, 'w', encoding='utf-8') as file:\n",
    "        file.write(content)\n",
    "\n",
    "# 设置文件夹路径\n",
    "neg_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\neg'\n",
    "pos_folder_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\pos'\n",
    "output_file_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews.txt'\n",
    "\n",
    "# 读取文件夹中的所有文件\n",
    "neg_files = read_files_from_folder(neg_folder_path)\n",
    "pos_files = read_files_from_folder(pos_folder_path)\n",
    "\n",
    "# 合并所有文件内容\n",
    "all_content = '\\n'.join(neg_files + pos_files)\n",
    "\n",
    "# 将合并后的内容写入新文件\n",
    "write_to_file(output_file_path, all_content)\n",
    "\n",
    "print(f\"所有评论已合并到文件：{output_file_path}\")"
   ],
   "id": "57427f9fdccf48c7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有评论已合并到文件：E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews.txt\n"
     ]
    }
   ],
   "execution_count": 3
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:13:26.523903Z",
     "start_time": "2025-06-12T08:13:26.505820Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "# 设置目录路径\n",
    "directory_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test'\n",
    "\n",
    "# 列出目录中的所有文件\n",
    "files_in_directory = os.listdir(directory_path)\n",
    "\n",
    "# 打印文件列表\n",
    "print(\"目录中的文件：\")\n",
    "for file in files_in_directory:\n",
    "    print(file)\n",
    "\n",
    "# 检查特定文件是否存在\n",
    "if 'combined_reviews.txt' in files_in_directory:\n",
    "    print(\"\\n文件 'combined_reviews.txt' 已成功保存在目录中。\")\n",
    "else:\n",
    "    print(\"\\n文件 'combined_reviews.txt' 未找到，请检查保存路径。\")"
   ],
   "id": "8bd11111783fc9f2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "目录中的文件：\n",
      "combined_reviews.txt\n",
      "combined_reviews_lower.txt\n",
      "labeledBow.feat\n",
      "neg\n",
      "pos\n",
      "urls_neg.txt\n",
      "urls_pos.txt\n",
      "\n",
      "文件 'combined_reviews.txt' 已成功保存在目录中。\n"
     ]
    }
   ],
   "execution_count": 4
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:13:27.762735Z",
     "start_time": "2025-06-12T08:13:26.768627Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 将合并后的评论文本转为小写\n",
    "with open(output_file_path, 'r', encoding='utf-8') as file:\n",
    "    content = file.read()\n",
    "\n",
    "lower_content = content.lower()\n",
    "\n",
    "# 可选择将小写内容写回文件或保存为新文件\n",
    "lower_output_file_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews_lower.txt'\n",
    "write_to_file(lower_output_file_path, lower_content)\n",
    "\n",
    "print(f\"所有评论已转为小写并保存到文件：{lower_output_file_path}\")"
   ],
   "id": "3f88497745ac764e",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "所有评论已转为小写并保存到文件：E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\aclImdb\\test\\combined_reviews_lower.txt\n"
     ]
    }
   ],
   "execution_count": 5
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:14:58.818159Z",
     "start_time": "2025-06-12T08:13:27.851260Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "import nltk\n",
    "\n",
    "# 下载缺失的 punkt_tab 资源\n",
    "nltk.download('punkt_tab')\n",
    "\n",
    "# 如果依然报错，可以尝试重新下载 punkt\n",
    "#nltk.download('punkt')\n",
    "# 下载所需的nltk资源（如未下载过）\n",
    "nltk.download('punkt')\n",
    "nltk.download('stopwords')\n",
    "\n",
    "# 读取小写化后的评论内容\n",
    "with open(lower_output_file_path, 'r', encoding='utf-8') as file:\n",
    "    text = file.read()\n",
    "\n",
    "# 分词\n",
    "tokens = word_tokenize(text)\n",
    "\n",
    "# 获取英文停用词列表\n",
    "stop_words = set(stopwords.words('english'))\n",
    "\n",
    "# 去除停用词\n",
    "filtered_tokens = [word for word in tokens if word.isalpha() and word not in stop_words]\n",
    "\n",
    "print(f\"分词后去除停用词的前100个词：\\n{filtered_tokens[:100]}\")"
   ],
   "id": "57b5e36043d15709",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt_tab to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt_tab is already up-to-date!\n",
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "分词后去除停用词的前100个词：\n",
      "['costner', 'dragged', 'movie', 'far', 'longer', 'necessary', 'aside', 'terrific', 'sea', 'rescue', 'sequences', 'care', 'characters', 'us', 'ghosts', 'closet', 'costner', 'character', 'realized', 'early', 'forgotten', 'much', 'later', 'time', 'care', 'character', 'really', 'care', 'cocky', 'overconfident', 'ashton', 'kutcher', 'problem', 'comes', 'kid', 'thinks', 'better', 'anyone', 'else', 'around', 'shows', 'signs', 'cluttered', 'closet', 'obstacle', 'appears', 'winning', 'costner', 'finally', 'well', 'past', 'half', 'way', 'point', 'stinker', 'costner', 'tells', 'us', 'kutcher', 'ghosts', 'told', 'kutcher', 'driven', 'best', 'prior', 'inkling', 'foreshadowing', 'magic', 'could', 'keep', 'turning', 'hour', 'example', 'majority', 'action', 'films', 'generic', 'boring', 'really', 'nothing', 'worth', 'watching', 'complete', 'waste', 'talents', 'ice', 'cube', 'proven', 'many', 'times', 'capable', 'acting', 'acting', 'well', 'bother', 'one', 'go', 'see', 'new', 'jack']\n"
     ]
    }
   ],
   "execution_count": 6
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:14:59.177853Z",
     "start_time": "2025-06-12T08:14:59.092516Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 检查 labels 是否正确定义且为列表或数组\n",
    "# labels 应该是与 reviews 数量一致的标签列表，例如：\n",
    "# labels = [0, 1, 0, 1, ...]  # 0 表示负面，1 表示正面\n",
    "\n",
    "# 如果你还没有定义 labels，请根据你的数据集生成它\n",
    "# 例如，如果 neg_files 和 pos_files 分别为负面和正面评论列表：\n",
    "labels = [0] * len(neg_files) + [1] * len(pos_files)\n",
    "\n",
    "# 重新合并评论和标签，确保一一对应\n",
    "reviews = neg_files + pos_files\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# 划分训练集和测试集\n",
    "train_reviews, test_reviews, train_labels, test_labels = train_test_split(\n",
    "    reviews, labels, test_size=0.2, random_state=42, stratify=labels\n",
    ")\n",
    "\n",
    "print(f\"训练集数量: {len(train_reviews)}\")\n",
    "print(f\"测试集数量: {len(test_reviews)}\")"
   ],
   "id": "db060b88de733644",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "训练集数量: 20000\n",
      "测试集数量: 5000\n"
     ]
    }
   ],
   "execution_count": 7
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:15:01.113473Z",
     "start_time": "2025-06-12T08:14:59.544506Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from collections import Counter\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 假设你有训练集和测试集的标签列表 train_labels 和 test_labels\n",
    "# 这里需要你根据实际情况替换变量名或加载标签数据\n",
    "\n",
    "# 示例：如果标签是 0（负面）和 1（正面）\n",
    "# train_labels = [...]\n",
    "# test_labels = [...]\n",
    "\n",
    "\n",
    "# 统计类别分布\n",
    "train_counter = Counter(train_labels)\n",
    "test_counter = Counter(test_labels)\n",
    "\n",
    "# 绘制柱状图\n",
    "fig, axes = plt.subplots(1, 2, figsize=(10, 4))\n",
    "\n",
    "axes[0].bar(train_counter.keys(), train_counter.values(), color=['skyblue', 'salmon'])\n",
    "axes[0].set_title('Train Set Class Distribution')\n",
    "axes[0].set_xlabel('Class')\n",
    "axes[0].set_ylabel('Count')\n",
    "\n",
    "axes[1].bar(test_counter.keys(), test_counter.values(), color=['skyblue', 'salmon'])\n",
    "axes[1].set_title('Test Set Class Distribution')\n",
    "axes[1].set_xlabel('Class')\n",
    "axes[1].set_ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ],
   "id": "f6de1ba5d9667b33",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 8
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:16:38.965645Z",
     "start_time": "2025-06-12T08:15:01.157605Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 定义 filtered_tokens，假设你要对所有评论进行分词和过滤\n",
    "import nltk\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# 确保已下载分词器\n",
    "nltk.download('punkt')\n",
    "\n",
    "# 合并所有评论文本\n",
    "all_text = ' '.join(reviews)\n",
    "\n",
    "# 分词\n",
    "tokens = word_tokenize(all_text)\n",
    "\n",
    "# 可选：去除标点和停用词\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "\n",
    "nltk.download('stopwords')\n",
    "stop_words = set(stopwords.words('english'))\n",
    "filtered_tokens = [word for word in tokens if word.isalpha() and word.lower() not in stop_words]"
   ],
   "id": "502dad15a1a443ef",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package punkt to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package punkt is already up-to-date!\n",
      "[nltk_data] Downloading package stopwords to\n",
      "[nltk_data]     C:\\Users\\任老大\\AppData\\Roaming\\nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n"
     ]
    }
   ],
   "execution_count": 9
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:16:45.682835Z",
     "start_time": "2025-06-12T08:16:39.136681Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 统计词频\n",
    "word_freq = Counter(filtered_tokens)\n",
    "top_10_words = word_freq.most_common(10)\n",
    "\n",
    "# 绘制Top-10高频词汇柱状图\n",
    "words, counts = zip(*top_10_words)\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(words, counts, color='skyblue')\n",
    "plt.title('Top-10 high')\n",
    "plt.xlabel('词汇')\n",
    "plt.ylabel('频率')\n",
    "plt.show()\n",
    "\n",
    "# 统计字符频率\n",
    "char_freq = Counter(''.join(filtered_tokens))\n",
    "top_10_chars = char_freq.most_common(10)\n",
    "\n",
    "# 绘制Top-10高频字符柱状图\n",
    "chars, char_counts = zip(*top_10_chars)\n",
    "plt.figure(figsize=(8, 4))\n",
    "plt.bar(chars, char_counts, color='salmon')\n",
    "plt.title('Top-10 high')\n",
    "plt.xlabel('字符')\n",
    "plt.ylabel('频率')\n",
    "plt.show()"
   ],
   "id": "8b5bdc79e24ac902",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39057 (\\N{CJK UNIFIED IDEOGRAPH-9891}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35789 (\\N{CJK UNIFIED IDEOGRAPH-8BCD}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27719 (\\N{CJK UNIFIED IDEOGRAPH-6C47}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtIAAAGHCAYAAACdwbVzAAAAOnRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjEwLjAsIGh0dHBzOi8vbWF0cGxvdGxpYi5vcmcvlHJYcgAAAAlwSFlzAAAPYQAAD2EBqD+naQAAPuFJREFUeJzt3XtcVWXe///3BgQBYYsQIEYeSknDPGAqWqF5wAqtnKkmjHI0tFvzMGqm35oym7Q8N9FBnW41tajJbCqNUFPTPJNMqXgqHbEgTBHUDBSu3x/+XHdbUHGNwjZez8djP2b2tT57rc+1t+z97mLthcMYYwQAAADgknhUdQMAAADA1YggDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AVczhcFTotmrVqkrpZ8aMGerdu7caNmwoh8OhTp06nbc2Ly9Pffv2VUhIiPz8/BQbG6sVK1ZU6DidOnVSdHT0Rev2798vh8OhuXPnVnAGrhwOh5544glbjwWAC/Gq6gYAoLpbv369y/0XXnhBK1eu1BdffOEy3qxZs0rp580335S/v7/uuOMOffLJJ+etKyoqUpcuXXT06FG98sorCg0N1WuvvaYePXpo+fLliouLuyz91K1bV+vXr9f1119/WfYHAJcLQRoAqlj79u1d7l9zzTXy8PAoM15ZduzYIQ+PM7+wvNCK8VtvvaVt27Zp3bp1io2NlSR17txZLVq00OjRo7Vx48bL0o+Pj0+VPRcAcCGc2gEAV4EjR45o0KBBqlevnry9vdWoUSM9/fTTKioqcqk7exrDzJkz1aRJE/n4+KhZs2ZKTU2t8LHOhuiLWbx4saKioqwQLUleXl56+OGHtWnTJv3www8V2s/mzZt12223yc/PT40aNdJLL72k0tJSa/v5Tu3417/+pZtvvlk+Pj5q1KiRXnnlFY0bN04Oh6Pc48yfP19NmzaVn5+fWrRooU8//bRC/QHA+bAiDQBu7tdff1Xnzp313Xff6fnnn9fNN9+sNWvWaOLEicrMzNSSJUtc6j/++GOtXLlS48ePl7+/v15//XU99NBD8vLy0h//+MfL1te2bdt02223lRm/+eabJUnbt29XvXr1LriP3Nxc9enTRyNHjtRzzz2nxYsXa+zYsYqIiNAjjzxy3selpaWpd+/euv322/Xee+/p9OnTmjJlin766ady65csWaLNmzdr/PjxqlWrliZNmqT77rtPu3btUqNGjS5h1gDwfwjSAODm5s2bp2+++Ubvv/++7r//fklSt27dVKtWLT311FNatmyZunXrZtX//PPP2rx5s8LCwiRJd911l6KjozV27NjLGqQPHz6sOnXqlBk/O3b48OEK7WPp0qVq27atJKlr165atWqV3nnnnQsG6WeffVb16tXT559/Lm9vb0lSjx491KBBg3LrT548qeXLlysgIECS1Lp1a0VEROj999/XmDFjLtonAJSHUzsAwM198cUX8vf3LxOC+/btK0llrpLRpUsXK0RLkqenpx588EHt3btXBw8elCSdPn3a5WaMsdXb+U6juNi2s8LDw60QfdbNN9+s//znP+d9zIkTJ7Rlyxbde++9VoiWpFq1aqlnz57lPqZz585WiJaksLAwhYaGXvA4AHAxBGkAcHOHDx9WeHh4mWAaGhoqLy+vMiu/4eHhZfZxduxsbY0aNVxu8+bNu+S+goODy111PnLkiCSVu1pd3j7O5ePjo5MnT573Mfn5+TLGuPzHwlnljdk9DgBcDKd2AICbCw4O1saNG2WMcQnTeXl5On36tEJCQlzqc3Nzy+zj7NjZQLl582aX7Q0bNrzkvpo3b65vv/22zPjZsYpcI9qOoKAgORyOcs+HLm/uAHClsCINAG6uS5cuOn78uD766COX8bffftva/lsrVqxwCZklJSV67733dP311+vaa6+VJLVp08blVt6K7cXcd9992rlzp8tl7k6fPq0FCxaoXbt2ioiIuOR9VoS/v7/atGmjjz76SMXFxdb48ePHuRIHgErFijQAuLlHHnlEr732mh599FHt379fzZs319q1azVhwgTddddd6tq1q0t9SEiI7rjjDv31r3+1rtqxc+fOCl8Cb8uWLdq/f78kqbCwUMYYffDBB5KkW265RfXr15ck9evXT6+99pruv/9+vfTSSwoNDdXrr7+uXbt2afny5ZfvCSjH+PHjdffddys+Pl7Dhg1TSUmJJk+erFq1almnlgDAlUaQBgA3V7NmTa1cuVJPP/20Jk+erEOHDqlevXoaNWqUnnvuuTL1vXr10k033aRnnnlGBw4c0PXXX6+FCxfqwQcfrNDxUlJSypwzffZqIXPmzLG+5Ojj46MVK1Zo9OjRGjJkiH755Re1bNlSn3322WX7q4bn06NHDy1atEjPPvusHnzwQYWHh2vQoEH68ccfNX/+/Ct6bAA4y2HsflUbAOB2HA6HBg8erJSUlKpupdKdOnVKLVu2VL169ZSenl7V7QCoBliRBgBclfr3769u3bqpbt26ys3N1ZtvvqmsrCy98sorVd0agGqCIA0AuCodO3ZMo0aN0qFDh1SjRg21bt1aS5cuLXPOOABcKZzaAQAAANjA5e8AAAAAGwjSAAAAgA0EaQAAAMAGvmxYyUpLS/Xjjz8qICDA5U/9AgAAwD0YY3Ts2DFFRETIw+P8684E6Ur2448/KjIysqrbAAAAwEVkZ2fr2muvPe92gnQlCwgIkHTmhQkMDKzibgAAAHCuwsJCRUZGWrntfAjSlezs6RyBgYEEaQAAADd2sdNw+bIhAAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwIYqDdJffvmlevbsqYiICDkcDn300Ucu240xGjdunCIiIuTr66tOnTpp+/btLjVFRUUaMmSIQkJC5O/vr169eungwYMuNfn5+UpKSpLT6ZTT6VRSUpKOHj3qUnPgwAH17NlT/v7+CgkJ0dChQ1VcXOxS8+233youLk6+vr6qV6+exo8fL2PMZXs+AAAAcPWo0iB94sQJtWjRQikpKeVunzRpkqZNm6aUlBRt3rxZ4eHh6tatm44dO2bVDB8+XIsXL1ZqaqrWrl2r48ePKyEhQSUlJVZNYmKiMjMzlZaWprS0NGVmZiopKcnaXlJSorvvvlsnTpzQ2rVrlZqaqkWLFmnkyJFWTWFhobp166aIiAht3rxZr776qqZMmaJp06ZdgWcGAAAAbs+4CUlm8eLF1v3S0lITHh5uXnrpJWvs119/NU6n07z55pvGGGOOHj1qatSoYVJTU62aH374wXh4eJi0tDRjjDE7duwwksyGDRusmvXr1xtJZufOncYYY5YuXWo8PDzMDz/8YNW8++67xsfHxxQUFBhjjHn99deN0+k0v/76q1UzceJEExERYUpLSys8z4KCAiPJ2i8AAADcS0XzmleVpvgL2Ldvn3Jzc9W9e3drzMfHR3FxcVq3bp0GDhyojIwMnTp1yqUmIiJC0dHRWrduneLj47V+/Xo5nU61a9fOqmnfvr2cTqfWrVunqKgorV+/XtHR0YqIiLBq4uPjVVRUpIyMDHXu3Fnr169XXFycfHx8XGrGjh2r/fv3q2HDhuXOo6ioSEVFRdb9wsLCy/L8XIqXtv5c6ce8HMa0CqnqFgAAAM7Lbb9smJubK0kKCwtzGQ8LC7O25ebmytvbW0FBQResCQ0NLbP/0NBQl5pzjxMUFCRvb+8L1py9f7amPBMnTrTOzXY6nYqMjLzwxAEAAHBVcNsgfZbD4XC5b4wpM3auc2vKq78cNeb//6LhhfoZO3asCgoKrFt2dvYFewcAAMDVwW2DdHh4uKSyq715eXnWSnB4eLiKi4uVn59/wZqffvqpzP4PHTrkUnPucfLz83Xq1KkL1uTl5Ukqu2r+Wz4+PgoMDHS5AQAA4OrntkG6YcOGCg8P17Jly6yx4uJirV69Wh06dJAkxcTEqEaNGi41OTk52rZtm1UTGxurgoICbdq0yarZuHGjCgoKXGq2bdumnJwcqyY9PV0+Pj6KiYmxar788kuXS+Klp6crIiJCDRo0uPxPAAAAANxalQbp48ePKzMzU5mZmZLOfMEwMzNTBw4ckMPh0PDhwzVhwgQtXrxY27ZtU9++feXn56fExERJktPpVP/+/TVy5EitWLFCW7du1cMPP6zmzZura9eukqSmTZuqR48eSk5O1oYNG7RhwwYlJycrISFBUVFRkqTu3burWbNmSkpK0tatW7VixQqNGjVKycnJ1gpyYmKifHx81LdvX23btk2LFy/WhAkTNGLEiIueagIAAIDfnyq9aseWLVvUuXNn6/6IESMkSY8++qjmzp2r0aNH6+TJkxo0aJDy8/PVrl07paenKyAgwHrM9OnT5eXlpQceeEAnT55Uly5dNHfuXHl6elo1Cxcu1NChQ62re/Tq1cvl2tWenp5asmSJBg0apI4dO8rX11eJiYmaMmWKVeN0OrVs2TINHjxYbdq0UVBQkEaMGGH1DAAAgOrFYQx/mq8yFRYWyul0qqCgoNLOl+bydwAAABVX0bzmtudIAwAAAO6MIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABrcO0qdPn9Yzzzyjhg0bytfXV40aNdL48eNVWlpq1RhjNG7cOEVERMjX11edOnXS9u3bXfZTVFSkIUOGKCQkRP7+/urVq5cOHjzoUpOfn6+kpCQ5nU45nU4lJSXp6NGjLjUHDhxQz5495e/vr5CQEA0dOlTFxcVXbP4AAABwX24dpF9++WW9+eabSklJUVZWliZNmqTJkyfr1VdftWomTZqkadOmKSUlRZs3b1Z4eLi6deumY8eOWTXDhw/X4sWLlZqaqrVr1+r48eNKSEhQSUmJVZOYmKjMzEylpaUpLS1NmZmZSkpKsraXlJTo7rvv1okTJ7R27VqlpqZq0aJFGjlyZOU8GQAAAHArDmOMqeomzichIUFhYWF66623rLE//OEP8vPz0/z582WMUUREhIYPH66nnnpK0pnV57CwML388ssaOHCgCgoKdM0112j+/Pl68MEHJUk//vijIiMjtXTpUsXHxysrK0vNmjXThg0b1K5dO0nShg0bFBsbq507dyoqKkqfffaZEhISlJ2drYiICElSamqq+vbtq7y8PAUGBlZoToWFhXI6nSooKKjwY/5bL239uVKOc7mNaRVS1S0AAIBqqKJ5za1XpG+99VatWLFCu3fvliT9+9//1tq1a3XXXXdJkvbt26fc3Fx1797deoyPj4/i4uK0bt06SVJGRoZOnTrlUhMREaHo6GirZv369XI6nVaIlqT27dvL6XS61ERHR1shWpLi4+NVVFSkjIyM886hqKhIhYWFLjcAAABc/byquoELeeqpp1RQUKAbb7xRnp6eKikp0YsvvqiHHnpIkpSbmytJCgsLc3lcWFiY/vOf/1g13t7eCgoKKlNz9vG5ubkKDQ0tc/zQ0FCXmnOPExQUJG9vb6umPBMnTtTzzz9/KdMGAADAVcCtV6Tfe+89LViwQO+8846+/vprzZs3T1OmTNG8efNc6hwOh8t9Y0yZsXOdW1NevZ2ac40dO1YFBQXWLTs7+4J9AQAA4Org1ivSTz75pMaMGaM//elPkqTmzZvrP//5jyZOnKhHH31U4eHhks6sFtetW9d6XF5enrV6HB4eruLiYuXn57usSufl5alDhw5WzU8//VTm+IcOHXLZz8aNG1225+fn69SpU2VWqn/Lx8dHPj4+dqYPAAAAN+bWK9K//PKLPDxcW/T09LQuf9ewYUOFh4dr2bJl1vbi4mKtXr3aCskxMTGqUaOGS01OTo62bdtm1cTGxqqgoECbNm2yajZu3KiCggKXmm3btiknJ8eqSU9Pl4+Pj2JiYi7zzAEAAODu3HpFumfPnnrxxRd13XXX6aabbtLWrVs1bdo09evXT9KZUy2GDx+uCRMmqHHjxmrcuLEmTJggPz8/JSYmSpKcTqf69++vkSNHKjg4WHXq1NGoUaPUvHlzde3aVZLUtGlT9ejRQ8nJyZo5c6YkacCAAUpISFBUVJQkqXv37mrWrJmSkpI0efJkHTlyRKNGjVJycnKlXX0DAAAA7sOtg/Srr76qv/71rxo0aJDy8vIUERGhgQMH6tlnn7VqRo8erZMnT2rQoEHKz89Xu3btlJ6eroCAAKtm+vTp8vLy0gMPPKCTJ0+qS5cumjt3rjw9Pa2ahQsXaujQodbVPXr16qWUlBRru6enp5YsWaJBgwapY8eO8vX1VWJioqZMmVIJzwQAAADcjVtfR/r3iOtIVxzXkQYAAFXhd3EdaQAAAMBdEaQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGxw+yD9ww8/6OGHH1ZwcLD8/PzUsmVLZWRkWNuNMRo3bpwiIiLk6+urTp06afv27S77KCoq0pAhQxQSEiJ/f3/16tVLBw8edKnJz89XUlKSnE6nnE6nkpKSdPToUZeaAwcOqGfPnvL391dISIiGDh2q4uLiKzZ3AAAAuC+3DtL5+fnq2LGjatSooc8++0w7duzQ1KlTVbt2batm0qRJmjZtmlJSUrR582aFh4erW7duOnbsmFUzfPhwLV68WKmpqVq7dq2OHz+uhIQElZSUWDWJiYnKzMxUWlqa0tLSlJmZqaSkJGt7SUmJ7r77bp04cUJr165VamqqFi1apJEjR1bKcwEAAAD34jDGmKpu4nzGjBmjr776SmvWrCl3uzFGERERGj58uJ566ilJZ1afw8LC9PLLL2vgwIEqKCjQNddco/nz5+vBBx+UJP3444+KjIzU0qVLFR8fr6ysLDVr1kwbNmxQu3btJEkbNmxQbGysdu7cqaioKH322WdKSEhQdna2IiIiJEmpqanq27ev8vLyFBgYWKE5FRYWyul0qqCgoMKP+W+9tPXnSjnO5TamVUhVtwAAAKqhiuY1t16R/vjjj9WmTRvdf//9Cg0NVatWrTR79mxr+759+5Sbm6vu3btbYz4+PoqLi9O6deskSRkZGTp16pRLTUREhKKjo62a9evXy+l0WiFaktq3by+n0+lSEx0dbYVoSYqPj1dRUZHLqSbnKioqUmFhocsNAAAAVz+3DtLff/+93njjDTVu3Fiff/65Hn/8cQ0dOlRvv/22JCk3N1eSFBYW5vK4sLAwa1tubq68vb0VFBR0wZrQ0NAyxw8NDXWpOfc4QUFB8vb2tmrKM3HiROu8a6fTqcjIyEt5CgAAAOCm3DpIl5aWqnXr1powYYJatWqlgQMHKjk5WW+88YZLncPhcLlvjCkzdq5za8qrt1NzrrFjx6qgoMC6ZWdnX7AvAAAAXB3cOkjXrVtXzZo1cxlr2rSpDhw4IEkKDw+XpDIrwnl5edbqcXh4uIqLi5Wfn3/Bmp9++qnM8Q8dOuRSc+5x8vPzderUqTIr1b/l4+OjwMBAlxsAAACufm4dpDt27Khdu3a5jO3evVv169eXJDVs2FDh4eFatmyZtb24uFirV69Whw4dJEkxMTGqUaOGS01OTo62bdtm1cTGxqqgoECbNm2yajZu3KiCggKXmm3btiknJ8eqSU9Pl4+Pj2JiYi7zzAEAAODuvKq6gQv5y1/+og4dOmjChAl64IEHtGnTJs2aNUuzZs2SdOZUi+HDh2vChAlq3LixGjdurAkTJsjPz0+JiYmSJKfTqf79+2vkyJEKDg5WnTp1NGrUKDVv3lxdu3aVdGaVu0ePHkpOTtbMmTMlSQMGDFBCQoKioqIkSd27d1ezZs2UlJSkyZMn68iRIxo1apSSk5NZZQYAAKiG3DpI33LLLVq8eLHGjh2r8ePHq2HDhpoxY4b69Olj1YwePVonT57UoEGDlJ+fr3bt2ik9PV0BAQFWzfTp0+Xl5aUHHnhAJ0+eVJcuXTR37lx5enpaNQsXLtTQoUOtq3v06tVLKSkp1nZPT08tWbJEgwYNUseOHeXr66vExERNmTKlEp4JAAAAuBu3vo707xHXka44riMNAACqwu/iOtIAAACAuyJIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGy4pD8Rfvr0aZWWlla43sPDQ15ebv1XyAEAAABbLinltm3bVrVr19bF/qq4w+GQMUYnTpzQpk2b/qsGAQAAAHd0SUHaGKMvvviiwvW33HLLJTcEAAAAXA0u6Rxph8NxSTu/1HoAAADgasGXDQEAAAAbCNIAAACADQRpAAAAwIZL+rJhcHCwOnToUOH6kJCQS24IAAAAuBpcUpBu06aN9u/fX+H6G2644VL7AQAAAK4KlxSkP//8c3300UcXvY70Wffff79eeOEFW40BAAAA7uySryN93XXXXVI9AAAA8HvEdaQBAAAAG7hqBwAAAGADQRoAAACw4ZLPkR4/fnyFawEAAIDfq0sK0q+//roKCwsrXB8fH3/JDQEAAABXg0sK0rGxsVeqDwAAAOCqwjnSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwwauqGwAuh5e2/lzVLdgyplVIVbcAAABsYkUaAAAAsOGqCtITJ06Uw+HQ8OHDrTFjjMaNG6eIiAj5+vqqU6dO2r59u8vjioqKNGTIEIWEhMjf31+9evXSwYMHXWry8/OVlJQkp9Mpp9OppKQkHT161KXmwIED6tmzp/z9/RUSEqKhQ4equLj4Sk0XAAAAbuyqCdKbN2/WrFmzdPPNN7uMT5o0SdOmTVNKSoo2b96s8PBwdevWTceOHbNqhg8frsWLFys1NVVr167V8ePHlZCQoJKSEqsmMTFRmZmZSktLU1pamjIzM5WUlGRtLykp0d13360TJ05o7dq1Sk1N1aJFizRy5MgrP3kAAAC4nasiSB8/flx9+vTR7NmzFRQUZI0bYzRjxgw9/fTT6t27t6KjozVv3jz98ssveueddyRJBQUFeuuttzR16lR17dpVrVq10oIFC/Ttt99q+fLlkqSsrCylpaXpH//4h2JjYxUbG6vZs2fr008/1a5duyRJ6enp2rFjhxYsWKBWrVqpa9eumjp1qmbPnq3CwsLz9l5UVKTCwkKXGwAAAK5+V0WQHjx4sO6++2517drVZXzfvn3Kzc1V9+7drTEfHx/FxcVp3bp1kqSMjAydOnXKpSYiIkLR0dFWzfr16+V0OtWuXTurpn379nI6nS410dHRioiIsGri4+NVVFSkjIyM8/Y+ceJE63QRp9OpyMjI/+KZAAAAgLtw+yCdmpqqr7/+WhMnTiyzLTc3V5IUFhbmMh4WFmZty83Nlbe3t8tKdnk1oaGhZfYfGhrqUnPucYKCguTt7W3VlGfs2LEqKCiwbtnZ2RebMgAAAK4Cbn35u+zsbA0bNkzp6emqWbPmeescDofLfWNMmbFznVtTXr2dmnP5+PjIx8fngr0AAADg6uPWK9IZGRnKy8tTTEyMvLy85OXlpdWrV+vvf/+7vLy8rBXic1eE8/LyrG3h4eEqLi5Wfn7+BWt++umnMsc/dOiQS825x8nPz9epU6fKrFQDAADg98+tg3SXLl307bffKjMz07q1adNGffr0UWZmpho1aqTw8HAtW7bMekxxcbFWr16tDh06SJJiYmJUo0YNl5qcnBxt27bNqomNjVVBQYE2bdpk1WzcuFEFBQUuNdu2bVNOTo5Vk56eLh8fH8XExFzR5wEAAADux61P7QgICFB0dLTLmL+/v4KDg63x4cOHa8KECWrcuLEaN26sCRMmyM/PT4mJiZIkp9Op/v37a+TIkQoODladOnU0atQoNW/e3PryYtOmTdWjRw8lJydr5syZkqQBAwYoISFBUVFRkqTu3burWbNmSkpK0uTJk3XkyBGNGjVKycnJCgwMrKynBAAAAG7CrYN0RYwePVonT57UoEGDlJ+fr3bt2ik9PV0BAQFWzfTp0+Xl5aUHHnhAJ0+eVJcuXTR37lx5enpaNQsXLtTQoUOtq3v06tVLKSkp1nZPT08tWbJEgwYNUseOHeXr66vExERNmTKl8iYLAAAAt+EwxpiqbqI6KSwslNPpVEFBQaWtZL+09edKOc7lNqZVSIVrq8McpeozTwAAqlJF85pbnyMNAAAAuCuCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABq+qbgAAfuulrT9XdQu2jGkVUtUtAAAqGSvSAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANjg1kF64sSJuuWWWxQQEKDQ0FDde++92rVrl0uNMUbjxo1TRESEfH191alTJ23fvt2lpqioSEOGDFFISIj8/f3Vq1cvHTx40KUmPz9fSUlJcjqdcjqdSkpK0tGjR11qDhw4oJ49e8rf318hISEaOnSoiouLr8jcAQAA4N7cOkivXr1agwcP1oYNG7Rs2TKdPn1a3bt314kTJ6yaSZMmadq0aUpJSdHmzZsVHh6ubt266dixY1bN8OHDtXjxYqWmpmrt2rU6fvy4EhISVFJSYtUkJiYqMzNTaWlpSktLU2ZmppKSkqztJSUluvvuu3XixAmtXbtWqampWrRokUaOHFk5TwYAAADciltfRzotLc3l/pw5cxQaGqqMjAzdfvvtMsZoxowZevrpp9W7d29J0rx58xQWFqZ33nlHAwcOVEFBgd566y3Nnz9fXbt2lSQtWLBAkZGRWr58ueLj45WVlaW0tDRt2LBB7dq1kyTNnj1bsbGx2rVrl6KiopSenq4dO3YoOztbERERkqSpU6eqb9++evHFFxUYGFiJzwwAAACqmluvSJ+roKBAklSnTh1J0r59+5Sbm6vu3btbNT4+PoqLi9O6deskSRkZGTp16pRLTUREhKKjo62a9evXy+l0WiFaktq3by+n0+lSEx0dbYVoSYqPj1dRUZEyMjLO23NRUZEKCwtdbgAAALj6XTVB2hijESNG6NZbb1V0dLQkKTc3V5IUFhbmUhsWFmZty83Nlbe3t4KCgi5YExoaWuaYoaGhLjXnHicoKEje3t5WTXkmTpxonXftdDoVGRl5KdMGAACAm7pqgvQTTzyhb775Ru+++26ZbQ6Hw+W+MabM2LnOrSmv3k7NucaOHauCggLrlp2dfcG+AAAAcHW4KoL0kCFD9PHHH2vlypW69tprrfHw8HBJKrMinJeXZ60eh4eHq7i4WPn5+Res+emnn8oc99ChQy415x4nPz9fp06dKrNS/Vs+Pj4KDAx0uQEAAODq59ZB2hijJ554Qh9++KG++OILNWzY0GV7w4YNFR4ermXLllljxcXFWr16tTp06CBJiomJUY0aNVxqcnJytG3bNqsmNjZWBQUF2rRpk1WzceNGFRQUuNRs27ZNOTk5Vk16erp8fHwUExNz+ScPAAAAt+bWV+0YPHiw3nnnHf3rX/9SQECAtSLsdDrl6+srh8Oh4cOHa8KECWrcuLEaN26sCRMmyM/PT4mJiVZt//79NXLkSAUHB6tOnToaNWqUmjdvbl3Fo2nTpurRo4eSk5M1c+ZMSdKAAQOUkJCgqKgoSVL37t3VrFkzJSUlafLkyTpy5IhGjRql5ORkVpkBXLKXtv5c1S3YMqZVSFW3AABuw62D9BtvvCFJ6tSpk8v4nDlz1LdvX0nS6NGjdfLkSQ0aNEj5+flq166d0tPTFRAQYNVPnz5dXl5eeuCBB3Ty5El16dJFc+fOlaenp1WzcOFCDR061Lq6R69evZSSkmJt9/T01JIlSzRo0CB17NhRvr6+SkxM1JQpU67Q7AEAAODO3DpIG2MuWuNwODRu3DiNGzfuvDU1a9bUq6++qldfffW8NXXq1NGCBQsueKzrrrtOn3766UV7AgAAwO+fW58jDQAAALgrgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADY4FXVDQAAfp9e2vpzVbdgy5hWIVXdAoCrBCvSAAAAgA2sSAMA8F9g5R2ovliRBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA182RAAAFwQX6gEykeQBgAAUPX4D4bqMMfKxKkdAAAAgA0EaQAAAMAGgjQAAABgA0EaAAAAsIEgDQAAANhAkAYAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGwgSAMAAAA2EKQBAAAAGwjSAAAAgA0EaQAAAMAGgjQAAABgA0Hahtdff10NGzZUzZo1FRMTozVr1lR1SwAAAKhkBOlL9N5772n48OF6+umntXXrVt1222268847deDAgapuDQAAAJWIIH2Jpk2bpv79++uxxx5T06ZNNWPGDEVGRuqNN96o6tYAAABQibyquoGrSXFxsTIyMjRmzBiX8e7du2vdunXlPqaoqEhFRUXW/YKCAklSYWHhlWv0HL8eP1Zpx7qcCgu9K1xbHeYoVY95Voc5StVjntVhjlL1mGd1mKNUPeZZHeZ4eY53JqcZYy5caFBhP/zwg5FkvvrqK5fxF1980TRp0qTcxzz33HNGEjdu3Lhx48aNG7er7JadnX3BbMiKtA0Oh8PlvjGmzNhZY8eO1YgRI6z7paWlOnLkiIKDg8/7mKtFYWGhIiMjlZ2drcDAwKpu54qoDnOUqsc8q8Mcpeoxz+owR6l6zLM6zFGqHvP8vc3RGKNjx44pIiLignUE6UsQEhIiT09P5ebmuozn5eUpLCys3Mf4+PjIx8fHZax27dpXqsUqERgY+Lv4obmQ6jBHqXrMszrMUaoe86wOc5Sqxzyrwxyl6jHP39McnU7nRWv4suEl8Pb2VkxMjJYtW+YyvmzZMnXo0KGKugIAAEBVYEX6Eo0YMUJJSUlq06aNYmNjNWvWLB04cECPP/54VbcGAACASkSQvkQPPvigDh8+rPHjxysnJ0fR0dFaunSp6tevX9WtVTofHx8999xzZU5d+T2pDnOUqsc8q8Mcpeoxz+owR6l6zLM6zFGqHvOsDnMsj8OYi13XAwAAAMC5OEcaAAAAsIEgDQAAANhAkAYAAABsIEijQjp16qThw4dXdRtua9y4cWrZsmVVt+HCGKMBAwaoTp06cjgcql27tstr2KBBA82YMaPK+sOl+e3P4LmvncPh0EcffVQlfV2Nfk//9vfv3y+Hw6HMzMzz1qxatUoOh0NHjx6ttL6q8rhXg3NfN56rqxdX7QAug1GjRmnIkCFV3YaLtLQ0zZ07V6tWrVKjRo3k4eEhX1/fqm4Ll8HmzZvl7+9f1W0ALjp16qSWLVta/5HSoUMH5eTkVOiPWgBXK4I0roji4mJ5e3tXdRuVplatWqpVq1ZVt+Hiu+++U926dfljQb9D11xzTVW3AFyUt7e3wsPDq7qNy666fb7hwji1AxV2+vRpPfHEE6pdu7aCg4P1zDPP6OzVExs0aKC//e1v6tu3r5xOp5KTk6usz06dOmnIkCEaPny4goKCFBYWplmzZunEiRP685//rICAAF1//fX67LPPrMesXr1abdu2lY+Pj+rWrasxY8bo9OnTkqSZM2eqXr16Ki0tdTlOr1699Oijj0oq/9SOOXPmqGnTpqpZs6ZuvPFGvf7661d24r/Rt29fDRkyRAcOHJDD4VCDBg0uenqOw+HQzJkzlZCQID8/PzVt2lTr16/X3r171alTJ/n7+ys2Nlbfffddpc2jIoqKijR06FCFhoaqZs2auvXWW7V582ZJ//fr0hUrVqhNmzby8/NThw4dtGvXLpd9fPLJJ4qJiVHNmjXVqFEjPf/889br744udmrC+PHjFRYWZv3aeN26dbr99tvl6+uryMhIDR06VCdOnKicZi/g2LFj6tOnj/z9/VW3bl1Nnz7d5d9pfn6+HnnkEQUFBcnPz0933nmn9uzZ47KPRYsW6aabbpKPj48aNGigqVOnumzPy8tTz5495evrq4YNG2rhwoVXdE6ffPKJateubb1fZGZmyuFw6Mknn7RqBg4cqIceeqhC/Zd32k7t2rU1d+7c8/awdOlSNWnSRL6+vurcubP2799/WeZ2IX379tXq1av1yiuvyOFwyOFwaO7cuS6nK8ydO1e1a9fWp59+qqioKPn5+emPf/yjTpw4oXnz5qlBgwYKCgrSkCFDVFJSYu27uLhYo0ePVr169eTv76927dpp1apVV3xOZ3Xq1ElPPPGERowYoZCQEHXr1k07duzQXXfdpVq1aiksLExJSUn6+eefrcekpaXp1ltvtT4vExISKvzeeeLECQUGBuqDDz5wGf/kk0/k7++vY8eOXdb5VcQHH3yg5s2by9fXV8HBweratav1HnKxz7offvhBDz74oIKCghQcHKx77rmnUv5NVhoDVEBcXJypVauWGTZsmNm5c6dZsGCB8fPzM7NmzTLGGFO/fn0TGBhoJk+ebPbs2WP27NlTpb0GBASYF154wezevdu88MILxsPDw9x5551m1qxZZvfu3eZ//ud/THBwsDlx4oQ5ePCg8fPzM4MGDTJZWVlm8eLFJiQkxDz33HPGGGMOHz5svL29zfLly61jHDlyxHh7e5vPP//cGGPMc889Z1q0aGFtnzVrlqlbt65ZtGiR+f77782iRYtMnTp1zNy5cyvlOTh69KgZP368ufbaa01OTo7Jy8szcXFxZtiwYVZN/fr1zfTp0637kky9evXMe++9Z3bt2mXuvfde06BBA3PHHXeYtLQ0s2PHDtO+fXvTo0ePSplDRQ0dOtRERESYpUuXmu3bt5tHH33UBAUFmcOHD5uVK1caSaZdu3Zm1apVZvv27ea2224zHTp0sB6flpZmAgMDzdy5c813331n0tPTTYMGDcy4ceOqcFZl/fb1K++1W7x4sSktLTVDhw411113ndm9e7cxxphvvvnG1KpVy0yfPt3s3r3bfPXVV6ZVq1amb9++VTALV4899pipX7++Wb58ufn222/NfffdZwICAqx59urVyzRt2tR8+eWXJjMz08THx5sbbrjBFBcXG2OM2bJli/Hw8DDjx483u3btMnPmzDG+vr5mzpw51jHuvPNOEx0dbdatW2e2bNliOnToYHx9fV2ev8vp6NGjxsPDw2zZssUYY8yMGTNMSEiIueWWW6yaJk2amDfeeKNC/Z99bX/L6XRaNfv27TOSzNatW40xxhw4cMD4+Pi4vFeHhYUZSSY/P/+KzPnsvGNjY01ycrLJyckxOTk5Zvny5S7HnTNnjqlRo4bp1q2b+frrr83q1atNcHCw6d69u3nggQfM9u3bzSeffGK8vb1Namqqte/ExETToUMH8+WXX5q9e/eayZMnGx8fH+vf+JV29vPvySefNDt37jTr1q0zISEhZuzYsSYrK8t8/fXXplu3bqZz587WYz744AOzaNEis3v3brN161bTs2dP07x5c1NSUmKMKfu6nX2vOvtcJScnm7vuusulj/vuu8888sgjlTLn3/rxxx+Nl5eXmTZtmtm3b5/55ptvzGuvvWaOHTt20c+6EydOmMaNG5t+/fqZb775xuzYscMkJiaaqKgoU1RUVOlzuRII0qiQuLg407RpU1NaWmqNPfXUU6Zp06bGmDMf7Pfee29VteciLi7O3Hrrrdb906dPG39/f5OUlGSN5eTkGElm/fr15v/9v/9noqKiXOb22muvmVq1allver169TL9+vWzts+cOdOEh4eb06dPG2PKBunIyEjzzjvvuPT1wgsvmNjY2Ms61wuZPn26qV+/vnW/IkH6mWeese6vX7/eSDJvvfWWNfbuu++amjVrXsm2L8nx48dNjRo1zMKFC62x4uJiExERYSZNmmR9OP32P4KWLFliJJmTJ08aY4y57bbbzIQJE1z2O3/+fFO3bt3KmUQFXSxI//Of/zQPP/ywufHGG012dra1LSkpyQwYMMBlX2vWrDEeHh7Wc1AVCgsLTY0aNcw///lPa+zo0aPGz8/PDBs2zOzevdtIMl999ZW1/eeffza+vr7m/fffN8acCVjdunVz2e+TTz5pmjVrZowxZteuXUaS2bBhg7U9KyvLSLpiQdoYY1q3bm2mTJlijDHm3nvvNS+++KLx9vY2hYWF1ntPVlbWRfs35tKD9NixY8t9r77SQdqYsu8x54bDOXPmGElm7969Vs3AgQONn5+fOXbsmDUWHx9vBg4caIwxZu/evcbhcJgffvjB5VhdunQxY8eOvXKT+Y24uDjTsmVL6/5f//pX0717d5ea7OxsI8ns2rWr3H3k5eUZSebbb781xlw8SG/cuNF4enpa8z506JCpUaOGWbVq1WWe3cVlZGQYSWb//v1ltl3ss+6tt94q8/laVFRkfH19rYWoqx2ndqDC2rdvL4fDYd2PjY3Vnj17rF/BtWnTpqpaK+Pmm2+2/r+np6eCg4PVvHlzaywsLEzSmV/7ZmVlKTY21mVuHTt21PHjx3Xw4EFJUp8+fbRo0SIVFRVJkhYuXKg//elP8vT0LHPsQ4cOKTs7W/3797fOna5Vq5b+9re/ud1pEef67fN29jk693n79ddfVVhYWOm9lee7777TqVOn1LFjR2usRo0aatu2rbKysqyx386rbt26ks689pKUkZGh8ePHu7xWycnJysnJ0S+//FJJM/nv/eUvf9H69eu1Zs0aXXvttdZ4RkaG5s6d6zK/+Ph4lZaWat++fVXW7/fff69Tp06pbdu21pjT6VRUVJQkKSsrS15eXmrXrp21PTg4WFFRUdZrm5WV5fLaS2d+ds++L53dx2/fm2688UbVrl37Cs7szKkAq1atkjFGa9as0T333KPo6GitXbtWK1euVFhYmG688caL9m9HVlZWue/V7sLPz0/XX3+9dT8sLEwNGjRw+Y5JWFiY9fP59ddfyxijJk2auPwbXr16daW+n/7231BGRoZWrlzp0s+NN94oSVZP3333nRITE9WoUSMFBgaqYcOGkqQDBw5U6Hht27bVTTfdpLfffluSNH/+fF133XW6/fbbL+e0KqRFixbq0qWLmjdvrvvvv1+zZ89Wfn5+hT7rMjIytHfvXgUEBFjb69Spo19//dXtPw8rii8b4rJxp6sI1KhRw+W+w+FwGTv7IVNaWipjjMuHjiTr3O+z4z179lRpaamWLFmiW265RWvWrNG0adPKPfbZcyNnz57tEgIklRu83Ul5z9H5njd3cO7r9Nvx345daA6lpaV6/vnn1bt37zL7r1mz5mXv+Urp1q2b3n33XX3++efq06ePNV5aWqqBAwdq6NChZR5z3XXXVWaLLi702v32f8t73NnHXOhn90LHuNI6deqkt956S//+97/l4eGhZs2aKS4uTqtXr1Z+fr7i4uKs/i7Uv3Sm93PHTp06dd5jn+95cxcXe28+O/bbn09PT09lZGSUef+szC94//bzrbS0VD179tTLL79cpu7sf6j37NlTkZGRmj17tiIiIlRaWqro6GgVFxdX+JiPPfaYUlJSNGbMGM2ZM0d//vOfK/3fsnTmc2vZsmVat26d0tPT9eqrr+rpp5/WJ598IunCn3WlpaWKiYkp97sJv5cvTROkUWEbNmwoc79x48ZuHw4vplmzZlq0aJHLh9q6desUEBCgevXqSZJ8fX3Vu3dvLVy4UHv37lWTJk0UExNT7v7CwsJUr149ff/99y6BBpffDTfcIG9vb61du1aJiYmSzoSMLVu2VPi6561bt9auXbt0ww03XMFOr7xevXqpZ8+eSkxMlKenp/70pz9JOjO/7du3u938rr/+etWoUUObNm1SZGSkJKmwsFB79uxRXFycmjVrptOnT2vjxo3WlWcOHz6s3bt3q2nTppLO/OyuXbvWZb/r1q1TkyZN5OnpqaZNm+r06dPasmWLtfK9a9euK36t3ttvv13Hjh3TjBkzFBcXJ4fDobi4OE2cOFH5+fkaNmxYhfqXzoSNnJwca/uePXsu+JuSZs2alfly4rnv3VeKt7e37ZX082nVqpVKSkqUl5en22677bLu267WrVtr0aJFatCggby8ysaow4cPKysrSzNnzrR6Pvd1roiHH35Yo0eP1t///ndt377d+nJ7VXA4HOrYsaM6duyoZ599VvXr19dXX3110c+61q1b67333lNoaKgCAwMruevKwakdqLDs7GyNGDFCu3bt0rvvvqtXX33V+kC4mg0aNEjZ2dkaMmSIdu7cqX/961967rnnNGLECHl4/N+PSJ8+fbRkyRL97//+rx5++OEL7nPcuHGaOHGiXnnlFe3evVvffvut5syZc95VbNjj7++v//mf/9GTTz6ptLQ07dixQ8nJyfrll1/Uv3//Cu3j2Wef1dtvv61x48Zp+/btysrK0nvvvadnnnnmCnd/+d13332aP3++/vznP1vf+H/qqae0fv16DR48WJmZmdqzZ48+/vjjKr/ueUBAgB599FE9+eSTWrlypbZv365+/frJw8NDDodDjRs31j333KPk5GStXbtW//73v/Xwww+rXr16uueeeyRJI0eO1IoVK/TCCy9o9+7dmjdvnlJSUjRq1ChJUlRUlHr06KHk5GRt3LhRGRkZeuyxx6749dSdTqdatmypBQsWqFOnTpLOhOuvv/5au3fvtsYu1r8k3XHHHUpJSdHXX3+tLVu26PHHHy+zgvtbjz/+uL777jvrvfqdd9654BU+LqcGDRpo48aN2r9/v37++efL8purJk2aqE+fPnrkkUf04Ycfat++fdq8ebNefvllLV269DJ0fekGDx6sI0eO6KGHHtKmTZv0/fffKz09Xf369VNJSYl1dYpZs2Zp7969+uKLLzRixIhLPk5QUJB69+6tJ598Ut27d3c5Zasybdy4URMmTNCWLVt04MABffjhhzp06JCaNm160c+6Pn36KCQkRPfcc4/WrFmjffv2afXq1Ro2bJh16uTVjiCNCnvkkUd08uRJtW3bVoMHD9aQIUM0YMCAqm7rv1avXj0tXbpUmzZtUosWLfT444+rf//+ZYLUHXfcoTp16mjXrl3W6uf5PPbYY/rHP/6huXPnqnnz5oqLi9PcuXOt8+Rw+bz00kv6wx/+oKSkJLVu3Vp79+7V559/rqCgoAo9Pj4+Xp9++qmWLVumW265Re3bt9e0adNUv379K9z5lfHHP/5R8+bNU1JSkj788EPdfPPNWr16tfbs2aPbbrtNrVq10l//+lfrV9BVadq0aYqNjVVCQoK6du2qjh07WpfRks5cVismJkYJCQmKjY2VMUZLly61gmTr1q31/vvvKzU1VdHR0Xr22Wc1fvx49e3b1zrGnDlzFBkZqbi4OPXu3VsDBgxQaGjoFZ9b586dVVJSYoXmoKAgNWvWTNdcc421ol6R/qdOnarIyEjdfvvtSkxM1KhRo+Tn53fe41533XVatGiRPvnkE7Vo0UJvvvmmJkyYcCWnahk1apQ8PT2teVb0fOCLmTNnjh555BGNHDlSUVFR6tWrlzZu3Gj9JqOyRURE6KuvvlJJSYni4+MVHR2tYcOGyel0ysPDQx4eHkpNTVVGRoaio6P1l7/8RZMnT7Z1rP79+6u4uFj9+vW7zLOouMDAQH355Ze666671KRJEz3zzDOaOnWq7rzzzot+1vn5+enLL7/Uddddp969e6tp06bq16+fTp48+btZoXYYdz+hCgBQLZw4cUL16tXT1KlTK/wbBeD3bOHChRo2bJh+/PFH/giMm+IcaQBAldi6dat27typtm3bqqCgQOPHj5ck69QNoLr65ZdftG/fPk2cOFEDBw4kRLsxTu0AAFSZKVOmqEWLFtZfSluzZo1CQkKqui2gSk2aNEktW7ZUWFiYxo4dW9Xt4AI4tQMAAACwgRVpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2ECQBgAAAGzgOtIAgItat26dBg0aVO62Hj16aMuWLfr555/L3b5p0yaugwvgd4kgDQC4qMLCQt17770aN26cy/j+/fs1ZswYHT9+XJmZmWUe16lTJ5WWllZOkwBQyTi1AwAAALCBIA0AAADYQJAGAAAAbCBIAwAAADYQpAEAAAAbCNIAAACADQRpAAAAwAaCNAAAAGADQRoAAACwgSANAAAA2MCfCAcAXJTT6dSnn36qTz/9tMy2+Ph4HT16VG3atCn3sR4erNkA+H1yGGNMVTcBAAAAXG1YJgAAAABsIEgDAAAANhCkAQAAABsI0gAAAIANBGkAAADABoI0AAAAYANBGgAAALCBIA0AAADYQJAGAAAAbPj/AF330rd5SRwhAAAAAElFTkSuQmCC"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23383 (\\N{CJK UNIFIED IDEOGRAPH-5B57}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31526 (\\N{CJK UNIFIED IDEOGRAPH-7B26}) missing from font(s) DejaVu Sans.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 10
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:16:48.619521Z",
     "start_time": "2025-06-12T08:16:45.738778Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import requests\n",
    "import os\n",
    "\n",
    "# 字体文件的下载链接\n",
    "font_url = 'https://noto-website-2.storage.googleapis.com/pkgs/NotoSansCJKsc-Regular.otf'\n",
    "\n",
    "# 字体文件保存的本地路径\n",
    "font_path = 'NotoSansCJKsc-Regular.otf'\n",
    "\n",
    "# 发送HTTP GET请求下载字体文件\n",
    "response = requests.get(font_url)\n",
    "\n",
    "# 检查请求是否成功\n",
    "if response.status_code == 200:\n",
    "    # 写入文件\n",
    "    with open(font_path, 'wb') as f:\n",
    "        f.write(response.content)\n",
    "    print(\"字体文件下载成功，保存在：\", font_path)\n",
    "else:\n",
    "    print(\"下载失败，状态码：\", response.status_code)"
   ],
   "id": "ff3bdb88370d134f",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "下载失败，状态码： 403\n"
     ]
    }
   ],
   "execution_count": 11
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:12.847905Z",
     "start_time": "2025-06-12T08:16:48.995107Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from wordcloud import WordCloud\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "# 设置字体路径（请根据实际下载路径修改）\n",
    "font_path = r'E:\\Ruanjian\\Python\\pycharm\\Ren\\Ren\\机器学习\\NotoSerifCJKsc'  # 或者你下载的NotoSerifCJKSC字体文件路径\n",
    "\n",
    "# 生成词云\n",
    "wordcloud = WordCloud(font_path=font_path, width=800, height=400, background_color='white').generate(' '.join(filtered_tokens))\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('词云 (Word Cloud)', fontproperties=plt.matplotlib.font_manager.FontProperties(fname=font_path))\n",
    "plt.show()\n",
    "\n",
    "# 生成字符云\n",
    "charcloud = WordCloud(font_path=font_path, width=800, height=400, background_color='white').generate(' '.join(''.join(filtered_tokens)))\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.imshow(charcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('字符云 (Char Cloud)', fontproperties=plt.matplotlib.font_manager.FontProperties(fname=font_path))\n",
    "plt.show()"
   ],
   "id": "266bec67ff11a2e",
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[12], line 8\u001B[0m\n\u001B[0;32m      5\u001B[0m font_path \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mr\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mE:\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mRuanjian\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mPython\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mpycharm\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mRen\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mRen\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124m机器学习\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mNotoSerifCJKsc\u001B[39m\u001B[38;5;124m'\u001B[39m  \u001B[38;5;66;03m# 或者你下载的NotoSerifCJKSC字体文件路径\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;66;03m# 生成词云\u001B[39;00m\n\u001B[1;32m----> 8\u001B[0m wordcloud \u001B[38;5;241m=\u001B[39m WordCloud(font_path\u001B[38;5;241m=\u001B[39mfont_path, width\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m800\u001B[39m, height\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m400\u001B[39m, background_color\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mwhite\u001B[39m\u001B[38;5;124m'\u001B[39m)\u001B[38;5;241m.\u001B[39mgenerate(\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(filtered_tokens))\n\u001B[0;32m     10\u001B[0m plt\u001B[38;5;241m.\u001B[39mfigure(figsize\u001B[38;5;241m=\u001B[39m(\u001B[38;5;241m12\u001B[39m, \u001B[38;5;241m6\u001B[39m))\n\u001B[0;32m     11\u001B[0m plt\u001B[38;5;241m.\u001B[39mimshow(wordcloud, interpolation\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mbilinear\u001B[39m\u001B[38;5;124m'\u001B[39m)\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\wordcloud\\wordcloud.py:642\u001B[0m, in \u001B[0;36mWordCloud.generate\u001B[1;34m(self, text)\u001B[0m\n\u001B[0;32m    627\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mgenerate\u001B[39m(\u001B[38;5;28mself\u001B[39m, text):\n\u001B[0;32m    628\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Generate wordcloud from text.\u001B[39;00m\n\u001B[0;32m    629\u001B[0m \n\u001B[0;32m    630\u001B[0m \u001B[38;5;124;03m    The input \"text\" is expected to be a natural text. If you pass a sorted\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    640\u001B[0m \u001B[38;5;124;03m    self\u001B[39;00m\n\u001B[0;32m    641\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 642\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgenerate_from_text(text)\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\wordcloud\\wordcloud.py:623\u001B[0m, in \u001B[0;36mWordCloud.generate_from_text\u001B[1;34m(self, text)\u001B[0m\n\u001B[0;32m    606\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mgenerate_from_text\u001B[39m(\u001B[38;5;28mself\u001B[39m, text):\n\u001B[0;32m    607\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Generate wordcloud from text.\u001B[39;00m\n\u001B[0;32m    608\u001B[0m \n\u001B[0;32m    609\u001B[0m \u001B[38;5;124;03m    The input \"text\" is expected to be a natural text. If you pass a sorted\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    621\u001B[0m \u001B[38;5;124;03m    self\u001B[39;00m\n\u001B[0;32m    622\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 623\u001B[0m     words \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mprocess_text(text)\n\u001B[0;32m    624\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mgenerate_from_frequencies(words)\n\u001B[0;32m    625\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\wordcloud\\wordcloud.py:598\u001B[0m, in \u001B[0;36mWordCloud.process_text\u001B[1;34m(self, text)\u001B[0m\n\u001B[0;32m    596\u001B[0m stopwords \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mset\u001B[39m([i\u001B[38;5;241m.\u001B[39mlower() \u001B[38;5;28;01mfor\u001B[39;00m i \u001B[38;5;129;01min\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mstopwords])\n\u001B[0;32m    597\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcollocations:\n\u001B[1;32m--> 598\u001B[0m     word_counts \u001B[38;5;241m=\u001B[39m unigrams_and_bigrams(words, stopwords, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mnormalize_plurals, \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mcollocation_threshold)\n\u001B[0;32m    599\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m    600\u001B[0m     \u001B[38;5;66;03m# remove stopwords\u001B[39;00m\n\u001B[0;32m    601\u001B[0m     words \u001B[38;5;241m=\u001B[39m [word \u001B[38;5;28;01mfor\u001B[39;00m word \u001B[38;5;129;01min\u001B[39;00m words \u001B[38;5;28;01mif\u001B[39;00m word\u001B[38;5;241m.\u001B[39mlower() \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m stopwords]\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\wordcloud\\tokenization.py:48\u001B[0m, in \u001B[0;36munigrams_and_bigrams\u001B[1;34m(words, stopwords, normalize_plurals, collocation_threshold)\u001B[0m\n\u001B[0;32m     45\u001B[0m n_words \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(unigrams)\n\u001B[0;32m     46\u001B[0m counts_unigrams, standard_form \u001B[38;5;241m=\u001B[39m process_tokens(\n\u001B[0;32m     47\u001B[0m     unigrams, normalize_plurals\u001B[38;5;241m=\u001B[39mnormalize_plurals)\n\u001B[1;32m---> 48\u001B[0m counts_bigrams, standard_form_bigrams \u001B[38;5;241m=\u001B[39m process_tokens(\n\u001B[0;32m     49\u001B[0m     [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m \u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;241m.\u001B[39mjoin(bigram) \u001B[38;5;28;01mfor\u001B[39;00m bigram \u001B[38;5;129;01min\u001B[39;00m bigrams],\n\u001B[0;32m     50\u001B[0m     normalize_plurals\u001B[38;5;241m=\u001B[39mnormalize_plurals)\n\u001B[0;32m     51\u001B[0m \u001B[38;5;66;03m# create a copy of counts_unigram so the score computation is not changed\u001B[39;00m\n\u001B[0;32m     52\u001B[0m orig_counts \u001B[38;5;241m=\u001B[39m counts_unigrams\u001B[38;5;241m.\u001B[39mcopy()\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\wordcloud\\tokenization.py:132\u001B[0m, in \u001B[0;36mprocess_tokens\u001B[1;34m(words, normalize_plurals)\u001B[0m\n\u001B[0;32m    129\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m word_lower, case_dict \u001B[38;5;129;01min\u001B[39;00m d\u001B[38;5;241m.\u001B[39mitems():\n\u001B[0;32m    130\u001B[0m     \u001B[38;5;66;03m# Get the most popular case.\u001B[39;00m\n\u001B[0;32m    131\u001B[0m     first \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mmax\u001B[39m(case_dict\u001B[38;5;241m.\u001B[39mitems(), key\u001B[38;5;241m=\u001B[39mitem1)[\u001B[38;5;241m0\u001B[39m]\n\u001B[1;32m--> 132\u001B[0m     fused_cases[first] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28msum\u001B[39m(case_dict\u001B[38;5;241m.\u001B[39mvalues())\n\u001B[0;32m    133\u001B[0m     standard_cases[word_lower] \u001B[38;5;241m=\u001B[39m first\n\u001B[0;32m    134\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m normalize_plurals:\n\u001B[0;32m    135\u001B[0m     \u001B[38;5;66;03m# add plurals to fused cases:\u001B[39;00m\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "execution_count": 12
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:46.567474Z",
     "start_time": "2025-06-12T08:17:20.913071Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "# 以IMDb为例，使用5000维词汇级特征\n",
    "tfidf_vectorizer_imdb = TfidfVectorizer(max_features=5000, tokenizer=word_tokenize)\n",
    "imdb_tfidf_matrix = tfidf_vectorizer_imdb.fit_transform([' '.join(filtered_tokens)])\n",
    "\n",
    "print(f\"IMDb TF-IDF 特征矩阵形状: {imdb_tfidf_matrix.shape}\")\n",
    "\n",
    "# 如果是THUCNews，使用字符级输入和3000维特征\n",
    "tfidf_vectorizer_thucnews = TfidfVectorizer(max_features=3000, analyzer='char')\n",
    "thucnews_tfidf_matrix = tfidf_vectorizer_thucnews.fit_transform([''.join(filtered_tokens)])\n",
    "\n",
    "print(f\"THUCNews TF-IDF 特征矩阵形状: {thucnews_tfidf_matrix.shape}\")"
   ],
   "id": "23b15e68bbfedf8a",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:517: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb TF-IDF 特征矩阵形状: (1, 5000)\n",
      "THUCNews TF-IDF 特征矩阵形状: (1, 68)\n"
     ]
    }
   ],
   "execution_count": 13
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:18:25.983150Z",
     "start_time": "2025-06-12T08:18:25.876411Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# 对IMDb的TF-IDF特征进行PCA降维，保留95%方差\n",
    "pca_imdb = PCA(n_components=0.95, svd_solver='full')\n",
    "imdb_tfidf_reduced = pca_imdb.fit_transform(imdb_tfidf_matrix.toarray())\n",
    "print(f\"IMDb TF-IDF 降维后形状: {imdb_tfidf_reduced.shape}\")\n",
    "\n",
    "# 对THUCNews的TF-IDF特征进行PCA降维，保留95%方差\n",
    "pca_thucnews = PCA(n_components=0.95, svd_solver='full')\n",
    "thucnews_tfidf_reduced = pca_thucnews.fit_transform(thucnews_tfidf_matrix.toarray())\n",
    "print(f\"THUCNews TF-IDF 降维后形状: {thucnews_tfidf_reduced.shape}\")\n",
    "\n",
    "# 词嵌入部分可使用如Word2Vec、GloVe等方法，具体实现可根据需求补充"
   ],
   "id": "d7b6e2abf9448ecf",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "IMDb TF-IDF 降维后形状: (1, 1)\n",
      "THUCNews TF-IDF 降维后形状: (1, 1)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\sklearn\\decomposition\\_pca.py:586: RuntimeWarning: invalid value encountered in divide\n",
      "  explained_variance_ = (S**2) / (n_samples - 1)\n",
      "E:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\sklearn\\decomposition\\_pca.py:586: RuntimeWarning: invalid value encountered in divide\n",
      "  explained_variance_ = (S**2) / (n_samples - 1)\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.147840100Z",
     "start_time": "2025-06-11T01:04:01.728591Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "d8f058cb06031fd1",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.148844600Z",
     "start_time": "2025-06-11T01:04:02.253925Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import os\n",
    "\n",
    "# 指定文件夹路径\n",
    "folder_path = r'E:\\Ruanjian\\Python\\Anaconda\\Lib\\site-packages'\n",
    "\n",
    "# 检查路径是否存在\n",
    "if os.path.exists(folder_path) and os.path.isdir(folder_path):\n",
    "    # 列出文件夹中的所有文件和子文件夹\n",
    "    files_and_folders = os.listdir(folder_path)\n",
    "    print(\"文件夹中的内容：\")\n",
    "    for item in files_and_folders:\n",
    "        print(item)\n",
    "else:\n",
    "    print(f\"路径不存在或不是一个文件夹：{folder_path}\")"
   ],
   "id": "3f5cd231b28ccab7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "路径不存在或不是一个文件夹：E:\\Ruanjian\\Python\\Anaconda\\Lib\\site-packages\n"
     ]
    }
   ],
   "execution_count": 14
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.148844600Z",
     "start_time": "2025-06-11T01:04:02.559253Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from gensim.models import KeyedVectors\n",
    "from wordcloud import WordCloud\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# IMDb: 加载预训练的英文Word2Vec（如Google News 300d）\n",
    "# 下载地址：https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/view?usp=sharing\n",
    "# 文件名通常为 GoogleNews-vectors-negative300.bin.gz\n",
    "imdb_w2v_path = r'E:\\Ruanjian\\Python\\Anaconda\\Lib\\site-packages\\gensim\\test\\test_data\\GoogleNews-vectors-negative300.bin.gz'\n",
    "imdb_w2v = KeyedVectors.load_word2vec_format(imdb_w2v_path, binary=True)\n",
    "\n",
    "# 获取IMDb评论的词向量平均值（300维）\n",
    "filtered_tokens = [\"example\", \"tokens\", \"here\"]  # 假设这是你的IMDb评论分词结果\n",
    "imdb_vectors = [imdb_w2v[word] for word in filtered_tokens if word in imdb_w2v]\n",
    "if imdb_vectors:\n",
    "    imdb_w2v_feature = np.mean(imdb_vectors, axis=0)\n",
    "    print(f\"IMDb Word2Vec 特征形状: {imdb_w2v_feature.shape}\")\n",
    "else:\n",
    "    print(\"IMDb评论中没有词在Word2Vec词表中。\")\n",
    "\n",
    "# 生成IMDb评论的词云\n",
    "imdb_wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(filtered_tokens))\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(imdb_wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('IMDb Reviews Word Cloud')\n",
    "plt.show()\n",
    "\n",
    "# THUCNews: 加载预训练的中文搜狗词向量（300维）\n",
    "# 下载地址：https://github.com/Embedding/Chinese-Word-Vectors\n",
    "# 文件名如 sgns.sogou.word\n",
    "thucnews_w2v_path = r'E:\\Ruanjian\\Python\\Anaconda\\Lib\\site-packages\\gensim\\test\\test_data\\sgns.sogou.word'\n",
    "thucnews_w2v = KeyedVectors.load_word2vec_format(thucnews_w2v_path, binary=False)\n",
    "\n",
    "# 获取THUCNews文本的词向量平均值（300维）\n",
    "# 假设thucnews_tokens为THUCNews分词后的列表\n",
    "thucnews_tokens = [\"这是\", \"一个\", \"示例\", \"文本\"]  # 假设这是你的THUCNews分词结果\n",
    "thucnews_vectors = [thucnews_w2v[word] for word in thucnews_tokens if word in thucnews_w2v]\n",
    "if thucnews_vectors:\n",
    "    thucnews_w2v_feature = np.mean(thucnews_vectors, axis=0)\n",
    "    print(f\"THUCNews 搜狗词向量特征形状: {thucnews_w2v_feature.shape}\")\n",
    "else:\n",
    "    print(\"THUCNews文本中没有词在搜狗词向量词表中。\")\n",
    "\n",
    "# 生成THUCNews文本的词云\n",
    "font_path = r'C:\\Windows\\Fonts\\simhei.ttf'  # 指定中文字体路径\n",
    "thucnews_wordcloud = WordCloud(width=800, height=400, background_color='white', font_path=font_path).generate(' '.join(thucnews_tokens))\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.imshow(thucnews_wordcloud, interpolation='bilinear')\n",
    "plt.axis('off')\n",
    "plt.title('THUCNews Text Word Cloud')\n",
    "plt.show()"
   ],
   "id": "f3544d2a7c54c879",
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'E:\\\\Ruanjian\\\\Python\\\\Anaconda\\\\Lib\\\\site-packages\\\\gensim\\\\test\\\\test_data\\\\GoogleNews-vectors-negative300.bin.gz'",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mFileNotFoundError\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[15], line 10\u001B[0m\n\u001B[0;32m      6\u001B[0m \u001B[38;5;66;03m# IMDb: 加载预训练的英文Word2Vec（如Google News 300d）\u001B[39;00m\n\u001B[0;32m      7\u001B[0m \u001B[38;5;66;03m# 下载地址：https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/view?usp=sharing\u001B[39;00m\n\u001B[0;32m      8\u001B[0m \u001B[38;5;66;03m# 文件名通常为 GoogleNews-vectors-negative300.bin.gz\u001B[39;00m\n\u001B[0;32m      9\u001B[0m imdb_w2v_path \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mr\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mE:\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mRuanjian\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mPython\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mAnaconda\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mLib\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124msite-packages\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mgensim\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mtest\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mtest_data\u001B[39m\u001B[38;5;124m\\\u001B[39m\u001B[38;5;124mGoogleNews-vectors-negative300.bin.gz\u001B[39m\u001B[38;5;124m'\u001B[39m\n\u001B[1;32m---> 10\u001B[0m imdb_w2v \u001B[38;5;241m=\u001B[39m KeyedVectors\u001B[38;5;241m.\u001B[39mload_word2vec_format(imdb_w2v_path, binary\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mTrue\u001B[39;00m)\n\u001B[0;32m     12\u001B[0m \u001B[38;5;66;03m# 获取IMDb评论的词向量平均值（300维）\u001B[39;00m\n\u001B[0;32m     13\u001B[0m filtered_tokens \u001B[38;5;241m=\u001B[39m [\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mexample\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtokens\u001B[39m\u001B[38;5;124m\"\u001B[39m, \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhere\u001B[39m\u001B[38;5;124m\"\u001B[39m]  \u001B[38;5;66;03m# 假设这是你的IMDb评论分词结果\u001B[39;00m\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\gensim\\models\\keyedvectors.py:1719\u001B[0m, in \u001B[0;36mKeyedVectors.load_word2vec_format\u001B[1;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header)\u001B[0m\n\u001B[0;32m   1672\u001B[0m \u001B[38;5;129m@classmethod\u001B[39m\n\u001B[0;32m   1673\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mload_word2vec_format\u001B[39m(\n\u001B[0;32m   1674\u001B[0m         \u001B[38;5;28mcls\u001B[39m, fname, fvocab\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, binary\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m, encoding\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mutf8\u001B[39m\u001B[38;5;124m'\u001B[39m, unicode_errors\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124mstrict\u001B[39m\u001B[38;5;124m'\u001B[39m,\n\u001B[0;32m   1675\u001B[0m         limit\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, datatype\u001B[38;5;241m=\u001B[39mREAL, no_header\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m   1676\u001B[0m     ):\n\u001B[0;32m   1677\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124;03m\"\"\"Load KeyedVectors from a file produced by the original C word2vec-tool format.\u001B[39;00m\n\u001B[0;32m   1678\u001B[0m \n\u001B[0;32m   1679\u001B[0m \u001B[38;5;124;03m    Warnings\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m   1717\u001B[0m \n\u001B[0;32m   1718\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m-> 1719\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m _load_word2vec_format(\n\u001B[0;32m   1720\u001B[0m         \u001B[38;5;28mcls\u001B[39m, fname, fvocab\u001B[38;5;241m=\u001B[39mfvocab, binary\u001B[38;5;241m=\u001B[39mbinary, encoding\u001B[38;5;241m=\u001B[39mencoding, unicode_errors\u001B[38;5;241m=\u001B[39municode_errors,\n\u001B[0;32m   1721\u001B[0m         limit\u001B[38;5;241m=\u001B[39mlimit, datatype\u001B[38;5;241m=\u001B[39mdatatype, no_header\u001B[38;5;241m=\u001B[39mno_header,\n\u001B[0;32m   1722\u001B[0m     )\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\gensim\\models\\keyedvectors.py:2048\u001B[0m, in \u001B[0;36m_load_word2vec_format\u001B[1;34m(cls, fname, fvocab, binary, encoding, unicode_errors, limit, datatype, no_header, binary_chunk_size)\u001B[0m\n\u001B[0;32m   2045\u001B[0m             counts[word] \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mint\u001B[39m(count)\n\u001B[0;32m   2047\u001B[0m logger\u001B[38;5;241m.\u001B[39minfo(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mloading projection weights from \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m, fname)\n\u001B[1;32m-> 2048\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m utils\u001B[38;5;241m.\u001B[39mopen(fname, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mrb\u001B[39m\u001B[38;5;124m'\u001B[39m) \u001B[38;5;28;01mas\u001B[39;00m fin:\n\u001B[0;32m   2049\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m no_header:\n\u001B[0;32m   2050\u001B[0m         \u001B[38;5;66;03m# deduce both vocab_size & vector_size from 1st pass over file\u001B[39;00m\n\u001B[0;32m   2051\u001B[0m         \u001B[38;5;28;01mif\u001B[39;00m binary:\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\smart_open\\smart_open_lib.py:235\u001B[0m, in \u001B[0;36mopen\u001B[1;34m(uri, mode, buffering, encoding, errors, newline, closefd, opener, ignore_ext, compression, transport_params)\u001B[0m\n\u001B[0;32m    232\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m \u001B[38;5;28;01mas\u001B[39;00m ve:\n\u001B[0;32m    233\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mNotImplementedError\u001B[39;00m(ve\u001B[38;5;241m.\u001B[39margs[\u001B[38;5;241m0\u001B[39m])\n\u001B[1;32m--> 235\u001B[0m binary \u001B[38;5;241m=\u001B[39m _open_binary_stream(uri, binary_mode, transport_params)\n\u001B[0;32m    236\u001B[0m decompressed \u001B[38;5;241m=\u001B[39m so_compression\u001B[38;5;241m.\u001B[39mcompression_wrapper(binary, binary_mode, compression)\n\u001B[0;32m    238\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mb\u001B[39m\u001B[38;5;124m'\u001B[39m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;129;01min\u001B[39;00m mode \u001B[38;5;129;01mor\u001B[39;00m explicit_encoding \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\smart_open\\smart_open_lib.py:398\u001B[0m, in \u001B[0;36m_open_binary_stream\u001B[1;34m(uri, mode, transport_params)\u001B[0m\n\u001B[0;32m    396\u001B[0m scheme \u001B[38;5;241m=\u001B[39m _sniff_scheme(uri)\n\u001B[0;32m    397\u001B[0m submodule \u001B[38;5;241m=\u001B[39m transport\u001B[38;5;241m.\u001B[39mget_transport(scheme)\n\u001B[1;32m--> 398\u001B[0m fobj \u001B[38;5;241m=\u001B[39m submodule\u001B[38;5;241m.\u001B[39mopen_uri(uri, mode, transport_params)\n\u001B[0;32m    399\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28mhasattr\u001B[39m(fobj, \u001B[38;5;124m'\u001B[39m\u001B[38;5;124mname\u001B[39m\u001B[38;5;124m'\u001B[39m):\n\u001B[0;32m    400\u001B[0m     fobj\u001B[38;5;241m.\u001B[39mname \u001B[38;5;241m=\u001B[39m uri\n",
      "File \u001B[1;32mE:\\Ruanjian\\Python\\anacondn\\envs\\jinke\\Lib\\site-packages\\smart_open\\local_file.py:34\u001B[0m, in \u001B[0;36mopen_uri\u001B[1;34m(uri_as_string, mode, transport_params)\u001B[0m\n\u001B[0;32m     32\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mopen_uri\u001B[39m(uri_as_string, mode, transport_params):\n\u001B[0;32m     33\u001B[0m     parsed_uri \u001B[38;5;241m=\u001B[39m parse_uri(uri_as_string)\n\u001B[1;32m---> 34\u001B[0m     fobj \u001B[38;5;241m=\u001B[39m io\u001B[38;5;241m.\u001B[39mopen(parsed_uri[\u001B[38;5;124m'\u001B[39m\u001B[38;5;124muri_path\u001B[39m\u001B[38;5;124m'\u001B[39m], mode)\n\u001B[0;32m     35\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m fobj\n",
      "\u001B[1;31mFileNotFoundError\u001B[0m: [Errno 2] No such file or directory: 'E:\\\\Ruanjian\\\\Python\\\\Anaconda\\\\Lib\\\\site-packages\\\\gensim\\\\test\\\\test_data\\\\GoogleNews-vectors-negative300.bin.gz'"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.148844600Z",
     "start_time": "2025-06-10T01:11:05.977531Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from gensim.models import KeyedVectors\n",
    "\n",
    "# IMDb: 加载预训练的英文Word2Vec（如Google News 300d）\n",
    "# 下载地址：https://code.google.com/archive/p/word2vec/\n",
    "# 文件名通常为 GoogleNews-vectors-negative300.bin.gz\n",
    "imdb_w2v_path = r'path_to\\GoogleNews-vectors-negative300.bin.gz'\n",
    "imdb_w2v = KeyedVectors.load_word2vec_format(imdb_w2v_path, binary=True)\n",
    "\n",
    "# 获取IMDb评论的词向量平均值（300维）\n",
    "imdb_vectors = [imdb_w2v[word] for word in filtered_tokens if word in imdb_w2v]\n",
    "if imdb_vectors:\n",
    "    imdb_w2v_feature = np.mean(imdb_vectors, axis=0)\n",
    "    print(f\"IMDb Word2Vec 特征形状: {imdb_w2v_feature.shape}\")\n",
    "else:\n",
    "    print(\"IMDb评论中没有词在Word2Vec词表中。\")\n",
    "\n",
    "# THUCNews: 加载预训练的中文搜狗词向量（300维）\n",
    "# 下载地址：https://github.com/Embedding/Chinese-Word-Vectors\n",
    "# 文件名如 sgns.sogou.word\n",
    "thucnews_w2v_path = r'path_to\\sgns.sogou.word'\n",
    "thucnews_w2v = KeyedVectors.load_word2vec_format(thucnews_w2v_path, binary=False)\n",
    "\n",
    "# 获取THUCNews文本的词向量平均值（300维）\n",
    "# 假设thucnews_tokens为THUCNews分词后的列表\n",
    "# thucnews_tokens = [...]\n",
    "thucnews_vectors = [thucnews_w2v[word] for word in thucnews_tokens if word in thucnews_w2v]\n",
    "if thucnews_vectors:\n",
    "    thucnews_w2v_feature = np.mean(thucnews_vectors, axis=0)\n",
    "    print(f\"THUCNews 搜狗词向量特征形状: {thucnews_w2v_feature.shape}\")\n",
    "else:\n",
    "    print(\"THUCNews文本中没有词在搜狗词向量词表中。\")"
   ],
   "id": "36bcd5ab803d0aad",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:21:56.543203Z",
     "start_time": "2025-06-12T08:18:46.330252Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import sklearn\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from sklearn.pipeline import Pipeline\n",
    "import time\n",
    "from sklearn.calibration import CalibratedClassifierCV\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "# 模型评估函数\n",
    "def evaluate_model(model, X_train, y_train, X_test, y_test, model_name):\n",
    "    \"\"\"训练并评估单个模型\"\"\"\n",
    "    print(f\"\\n正在训练 {model_name} 模型...\")\n",
    "    start_time = time.time()\n",
    "    \n",
    "    # 创建pipeline\n",
    "    pipeline = Pipeline([\n",
    "        ('tfidf', TfidfVectorizer(max_features=10000, stop_words='english')),\n",
    "        ('clf', model)\n",
    "    ])\n",
    "    \n",
    "    # 训练模型\n",
    "    pipeline.fit(X_train, y_train)\n",
    "    \n",
    "    # 预测\n",
    "    y_pred = pipeline.predict(X_test)\n",
    "    y_pred_prob = pipeline.predict_proba(X_test)[:, 1] if hasattr(pipeline, \"predict_proba\") else None\n",
    "    \n",
    "    # 评估\n",
    "    accuracy = accuracy_score(y_test, y_pred)\n",
    "    precision = precision_score(y_test, y_pred, average='weighted')\n",
    "    recall = recall_score(y_test, y_pred, average='weighted')\n",
    "    f1 = f1_score(y_test, y_pred, average='weighted')\n",
    "    train_time = time.time() - start_time\n",
    "    \n",
    "    print(f\"{model_name} 训练时间: {train_time:.2f}秒\")\n",
    "    print(f\"{model_name} 分类报告:\")\n",
    "    print(classification_report(y_test, y_pred))\n",
    "    \n",
    "    return {\n",
    "        'Model': model_name,\n",
    "        'Train Time(s)': round(train_time, 2),\n",
    "        'Accuracy': accuracy,\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-Score': f1\n",
    "    }, y_pred, y_pred_prob\n",
    "\n",
    "\n",
    "# 检测sklearn版本并使用兼容的CalibratedClassifierCV参数\n",
    "sklearn_version = sklearn.__version__\n",
    "if int(sklearn_version.split('.')[1]) >= 24:  # 0.24+版本\n",
    "    base_estimator_param = 'base_estimator'\n",
    "else:  # 0.23及以下版本\n",
    "    base_estimator_param = 'estimator'\n",
    "\n",
    "# 使用CalibratedClassifierCV获取概率估计，兼容不同sklearn版本\n",
    "svm_model = CalibratedClassifierCV(\n",
    "    **{base_estimator_param: LinearSVC(C=1.0, random_state=42)},\n",
    "    cv=3\n",
    ")\n",
    "\n",
    "# 定义要评估的模型\n",
    "models = [\n",
    "    (\"Logistic Regression\", LogisticRegression(max_iter=1000)),\n",
    "    (\"SVM (Probabilistic)\", svm_model),\n",
    "    (\"Decision Tree\", DecisionTreeClassifier()),\n",
    "    (\"Random Forest\", RandomForestClassifier()),\n",
    "    (\"KNN\", KNeighborsClassifier()),\n",
    "    (\"Naive Bayes\", MultinomialNB())\n",
    "]\n",
    "\n",
    "# 存储结果的DataFrame\n",
    "results = pd.DataFrame(columns=['Model', 'Train Time(s)', 'Accuracy', 'Precision', 'Recall', 'F1-Score'])\n",
    "\n",
    "# 存储预测结果和概率\n",
    "predictions = {}\n",
    "pred_probs = {}\n",
    "\n",
    "# 训练并评估每个模型\n",
    "for name, model in models:\n",
    "    model_result, y_pred, y_pred_prob = evaluate_model(\n",
    "        model, train_reviews, train_labels, test_reviews, test_labels, name\n",
    "    )\n",
    "    results.loc[len(results)] = model_result\n",
    "    predictions[name] = y_pred\n",
    "    pred_probs[name] = y_pred_prob"
   ],
   "id": "851f1277cf3247f9",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "正在训练 Logistic Regression 模型...\n",
      "Logistic Regression 训练时间: 10.45秒\n",
      "Logistic Regression 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.89      0.90      2500\n",
      "           1       0.89      0.91      0.90      2500\n",
      "\n",
      "    accuracy                           0.90      5000\n",
      "   macro avg       0.90      0.90      0.90      5000\n",
      "weighted avg       0.90      0.90      0.90      5000\n",
      "\n",
      "\n",
      "正在训练 SVM (Probabilistic) 模型...\n",
      "SVM (Probabilistic) 训练时间: 11.29秒\n",
      "SVM (Probabilistic) 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.89      0.89      2500\n",
      "           1       0.89      0.90      0.90      2500\n",
      "\n",
      "    accuracy                           0.90      5000\n",
      "   macro avg       0.90      0.90      0.90      5000\n",
      "weighted avg       0.90      0.90      0.90      5000\n",
      "\n",
      "\n",
      "正在训练 Decision Tree 模型...\n",
      "Decision Tree 训练时间: 50.66秒\n",
      "Decision Tree 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.71      0.72      0.72      2500\n",
      "           1       0.72      0.71      0.71      2500\n",
      "\n",
      "    accuracy                           0.71      5000\n",
      "   macro avg       0.71      0.71      0.71      5000\n",
      "weighted avg       0.71      0.71      0.71      5000\n",
      "\n",
      "\n",
      "正在训练 Random Forest 模型...\n",
      "Random Forest 训练时间: 81.42秒\n",
      "Random Forest 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.87      0.86      2500\n",
      "           1       0.87      0.84      0.85      2500\n",
      "\n",
      "    accuracy                           0.86      5000\n",
      "   macro avg       0.86      0.86      0.86      5000\n",
      "weighted avg       0.86      0.86      0.86      5000\n",
      "\n",
      "\n",
      "正在训练 KNN 模型...\n",
      "KNN 训练时间: 27.24秒\n",
      "KNN 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.80      0.74      0.77      2500\n",
      "           1       0.76      0.81      0.78      2500\n",
      "\n",
      "    accuracy                           0.78      5000\n",
      "   macro avg       0.78      0.78      0.78      5000\n",
      "weighted avg       0.78      0.78      0.78      5000\n",
      "\n",
      "\n",
      "正在训练 Naive Bayes 模型...\n",
      "Naive Bayes 训练时间: 8.51秒\n",
      "Naive Bayes 分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.87      0.89      0.88      2500\n",
      "           1       0.89      0.87      0.88      2500\n",
      "\n",
      "    accuracy                           0.88      5000\n",
      "   macro avg       0.88      0.88      0.88      5000\n",
      "weighted avg       0.88      0.88      0.88      5000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 15
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.149842300Z",
     "start_time": "2025-06-11T01:09:17.705845Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "# 确保中文字体正确显示（添加字体配置）\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\", \"Microsoft YaHei\"]  # 使用系统存在的中文字体\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  # 解决负号显示问题\n",
    "\n",
    "def plot_confusion_matrix(y_true, y_pred, model_name):\n",
    "    \"\"\"\n",
    "    绘制混淆矩阵（优化版）\n",
    "    \n",
    "    参数:\n",
    "    y_true: 真实标签\n",
    "    y_pred: 预测标签\n",
    "    model_name: 模型名称\n",
    "    \"\"\"\n",
    "    try:\n",
    "        # 计算混淆矩阵\n",
    "        cm = confusion_matrix(y_true, y_pred)\n",
    "        \n",
    "        # 创建画布\n",
    "        plt.figure(figsize=(8, 6))\n",
    "        \n",
    "        # 绘制热力图（使用Seaborn的改进版）\n",
    "        sns.heatmap(\n",
    "            cm,\n",
    "            annot=True,\n",
    "            fmt='d',\n",
    "            cmap='Blues',\n",
    "            xticklabels=['负面', '正面'],\n",
    "            yticklabels=['负面', '正面'],\n",
    "            linewidths=0.5,  # 添加边框间距\n",
    "            annot_kws={'fontsize': 12}  # 调整文本大小\n",
    "        )\n",
    "        \n",
    "        # 设置标签和标题\n",
    "        plt.xlabel('预测标签', fontsize=14)\n",
    "        plt.ylabel('真实标签', fontsize=14)\n",
    "        plt.title(f'{model_name} 混淆矩阵', fontsize=16, pad=20)  # 增加标题间距\n",
    "        \n",
    "\n",
    "        # 显示图像\n",
    "        plt.show()\n",
    "        \n",
    "    except Exception as e:\n",
    "        print(f\"绘制混淆矩阵时出错 ({model_name}): {str(e)}\")\n",
    "        plt.close()  # 关闭异常图像避免占用内存\n",
    "\n",
    "# 为每个模型绘制混淆矩阵（添加异常处理）\n",
    "for model_name, y_pred in predictions.items():\n",
    "    try:\n",
    "        plot_confusion_matrix(test_labels, y_pred, model_name)\n",
    "    except Exception as e:\n",
    "        print(f\"处理 {model_name} 时出错: {str(e)}\")\n",
    "        continue"
   ],
   "id": "6a896e1e28892f73",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 17
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.150841600Z",
     "start_time": "2025-06-11T01:09:22.434517Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def compare_models(results):\n",
    "    \"\"\"比较不同模型的性能\"\"\"\n",
    "    # 设置图表\n",
    "    plt.figure(figsize=(15, 10))\n",
    "    \n",
    "    # 1. 训练时间比较\n",
    "    plt.subplot(2, 2, 1)\n",
    "    sns.barplot(x='Train Time(s)', y='Model', data=results)\n",
    "    plt.title('模型训练时间比较')\n",
    "    \n",
    "    # 2. 准确率比较\n",
    "    plt.subplot(2, 2, 2)\n",
    "    sns.barplot(x='Accuracy', y='Model', data=results)\n",
    "    plt.title('模型准确率比较')\n",
    "    \n",
    "    # 3. 综合性能比较\n",
    "    metrics = ['Accuracy', 'Precision', 'Recall', 'F1-Score']\n",
    "    results_melted = results.melt(id_vars='Model', value_vars=metrics, var_name='Metric', value_name='Score')\n",
    "    \n",
    "    plt.subplot(2, 2, 3)\n",
    "    sns.barplot(x='Score', y='Model', hue='Metric', data=results_melted)\n",
    "    plt.title('模型综合性能比较')\n",
    "    plt.legend(loc='lower right')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\\n模型性能比较结果:\")\n",
    "    print(results.sort_values(by='Accuracy', ascending=False))\n",
    "\n",
    "# 执行模型比较\n",
    "compare_models(results)"
   ],
   "id": "c61d25fa13a6416f",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1500x1000 with 3 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能比较结果:\n",
      "                 Model  Train Time(s)  Accuracy  Precision  Recall  F1-Score\n",
      "0  Logistic Regression          10.82    0.8986   0.898870  0.8986  0.898583\n",
      "1  SVM (Probabilistic)          12.43    0.8954   0.895469  0.8954  0.895395\n",
      "5          Naive Bayes          17.85    0.8770   0.877210  0.8770  0.876983\n",
      "3        Random Forest         109.23    0.8612   0.861408  0.8612  0.861180\n",
      "4                  KNN          39.13    0.7758   0.777221  0.7758  0.775512\n",
      "2        Decision Tree          63.17    0.7192   0.719202  0.7192  0.719199\n"
     ]
    }
   ],
   "execution_count": 18
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.150841600Z",
     "start_time": "2025-06-11T01:09:25.070361Z"
    }
   },
   "cell_type": "code",
   "source": [
    "def compare_models(results):\n",
    "    \"\"\"比较不同模型的性能（详细版）\"\"\"\n",
    "    # 设置图表\n",
    "    plt.figure(figsize=(18, 12))\n",
    "    \n",
    "    # 1. 训练时间比较\n",
    "    plt.subplot(2, 2, 1)\n",
    "    ax1 = sns.barplot(x='Train Time(s)', y='Model', data=results)\n",
    "    plt.title('模型训练时间比较')\n",
    "    plt.xlabel('训练时间 (秒)')\n",
    "    plt.ylabel('模型')\n",
    "    \n",
    "    # 在训练时间柱状图上显示对应的值\n",
    "    for p in ax1.patches:\n",
    "        width = p.get_width()  # 获取柱子的宽度（即值）\n",
    "        plt.text(width + 0.01,  # 文本的 x 位置\n",
    "                 p.get_y() + p.get_height() / 2,  # 文本的 y 位置\n",
    "                 f'{width:.2f}',  # 显示的文本内容，保留两位小数\n",
    "                 ha='left',  # 水平对齐方式\n",
    "                 va='center')  # 垂直对齐方式\n",
    "    \n",
    "    # 2. 准确率比较\n",
    "    plt.subplot(2, 2, 2)\n",
    "    ax2 = sns.barplot(x='Accuracy', y='Model', data=results)\n",
    "    plt.title('模型准确率比较')\n",
    "    plt.xlabel('准确率')\n",
    "    plt.ylabel('模型')\n",
    "    \n",
    "    # 在准确率柱状图上显示对应的值\n",
    "    for p in ax2.patches:\n",
    "        width = p.get_width()  # 获取柱子的宽度（即值）\n",
    "        plt.text(width + 0.01,  # 文本的 x 位置\n",
    "                 p.get_y() + p.get_height() / 2,  # 文本的 y 位置\n",
    "                 f'{width:.2f}',  # 显示的文本内容，保留两位小数\n",
    "                 ha='left',  # 水平对齐方式\n",
    "                 va='center')  # 垂直对齐方式\n",
    "    \n",
    "\n",
    "\n",
    "    \n",
    "    print(\"\\n模型性能比较结果（按准确率排序）:\")\n",
    "    print(results.sort_values(by='Accuracy', ascending=False))\n",
    "\n",
    "\n",
    "def compare_models_detailed(results):\n",
    "    \"\"\"比较不同模型的性能\"\"\"\n",
    "    # 设置图表\n",
    "    plt.figure(figsize=(18, 12))\n",
    "    \n",
    "    # 定义要绘制的指标\n",
    "    metrics = ['Recall', 'Accuracy', 'Precision', 'F1-Score']\n",
    "    \n",
    "    # 为每个指标绘制一个柱状图\n",
    "    for i, metric in enumerate(metrics, start=1):\n",
    "        plt.subplot(2, 2, i)\n",
    "        ax = sns.barplot(x=metric, y='Model', data=results)  # 绘制柱状图\n",
    "        plt.title(f'模型 {metric} 比较')\n",
    "        plt.xlabel(metric)\n",
    "        plt.ylabel('模型')\n",
    "        \n",
    "        # 在柱状图上显示对应的值\n",
    "        for p in ax.patches:\n",
    "            width = p.get_width()  # 获取柱子的宽度（即值）\n",
    "            plt.text(width + 0.01,  # 文本的 x 位置\n",
    "                     p.get_y() + p.get_height() / 2,  # 文本的 y 位置\n",
    "                     f'{width:.2f}',  # 显示的文本内容，保留两位小数\n",
    "                     ha='left',  # 水平对齐方式\n",
    "                     va='center') \n",
    "            plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    print(\"\\n模型性能比较结果（按准确率排序）:\")\n",
    "    print(results.sort_values(by='Accuracy', ascending=False))\n",
    "\n",
    "# 执行详细模型比较\n",
    "compare_models_detailed(results)\n",
    "# 执行模型比较\n",
    "compare_models(results)# 垂直对齐方式\n",
    "    \n",
    " \n",
    "   "
   ],
   "id": "3de553f28914d335",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 4 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能比较结果（按准确率排序）:\n",
      "                 Model  Train Time(s)  Accuracy  Precision  Recall  F1-Score\n",
      "0  Logistic Regression          10.82    0.8986   0.898870  0.8986  0.898583\n",
      "1  SVM (Probabilistic)          12.43    0.8954   0.895469  0.8954  0.895395\n",
      "5          Naive Bayes          17.85    0.8770   0.877210  0.8770  0.876983\n",
      "3        Random Forest         109.23    0.8612   0.861408  0.8612  0.861180\n",
      "4                  KNN          39.13    0.7758   0.777221  0.7758  0.775512\n",
      "2        Decision Tree          63.17    0.7192   0.719202  0.7192  0.719199\n",
      "\n",
      "模型性能比较结果（按准确率排序）:\n",
      "                 Model  Train Time(s)  Accuracy  Precision  Recall  F1-Score\n",
      "0  Logistic Regression          10.82    0.8986   0.898870  0.8986  0.898583\n",
      "1  SVM (Probabilistic)          12.43    0.8954   0.895469  0.8954  0.895395\n",
      "5          Naive Bayes          17.85    0.8770   0.877210  0.8770  0.876983\n",
      "3        Random Forest         109.23    0.8612   0.861408  0.8612  0.861180\n",
      "4                  KNN          39.13    0.7758   0.777221  0.7758  0.775512\n",
      "2        Decision Tree          63.17    0.7192   0.719202  0.7192  0.719199\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1800x1200 with 2 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 19
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.152117300Z",
     "start_time": "2025-06-11T01:09:32.673080Z"
    }
   },
   "cell_type": "code",
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "def plot_roc_curves(y_test, pred_probs):\n",
    "    \"\"\"绘制所有模型的ROC曲线\"\"\"\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    \n",
    "    for model_name, y_pred_prob in pred_probs.items():\n",
    "        if y_pred_prob is not None:\n",
    "            fpr, tpr, _ = roc_curve(y_test, y_pred_prob)\n",
    "            roc_auc = auc(fpr, tpr)\n",
    "            plt.plot(fpr, tpr, lw=2, label=f'{model_name} (AUC = {roc_auc:.3f})')\n",
    "    \n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('假正例率')\n",
    "    plt.ylabel('真正例率')\n",
    "    plt.title('各模型ROC曲线比较')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "# 绘制ROC曲线\n",
    "plot_roc_curves(test_labels, pred_probs)"
   ],
   "id": "e77be7d87fe02882",
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:26:27.591628Z",
     "start_time": "2025-06-12T08:23:19.548300Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# Sigmoid函数\n",
    "def sigmoid(z):\n",
    "    return 1 / (1 + np.exp(-z))\n",
    "\n",
    "# 手写逻辑回归模型\n",
    "class MyLogisticRegression:\n",
    "    def __init__(self, learning_rate=0.01, max_iter=1000):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.max_iter = max_iter\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "        self.weights = np.zeros(n_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        # 梯度下降\n",
    "        for _ in range(self.max_iter):\n",
    "            model = np.dot(X, self.weights) + self.bias\n",
    "            predictions = sigmoid(model)\n",
    "\n",
    "            # 计算梯度\n",
    "            dw = (1 / n_samples) * np.dot(X.T, (predictions - y))\n",
    "            db = (1 / n_samples) * np.sum(predictions - y)\n",
    "\n",
    "            # 更新权重和偏置\n",
    "            self.weights -= self.learning_rate * dw\n",
    "            self.bias -= self.learning_rate * db\n",
    "\n",
    "    def predict(self, X):\n",
    "        model = np.dot(X, self.weights) + self.bias\n",
    "        predictions = sigmoid(model)\n",
    "        return np.round(predictions)\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews).toarray()  # 转换为numpy数组\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews).toarray()\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练手写的逻辑回归模型并计时\n",
    "start_time = time.time()\n",
    "my_lr = MyLogisticRegression(learning_rate=0.01, max_iter=1000)\n",
    "my_lr.fit(X_train, y_train)\n",
    "train_time_my_lr = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = my_lr.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_my_lr = accuracy_score(y_test, y_pred)\n",
    "precision_my_lr = precision_score(y_test, y_pred)\n",
    "mse_my_lr = mean_squared_error(y_test, y_pred)\n",
    "recall_my_lr = recall_score(y_test, y_pred)\n",
    "f1_my_lr = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy_my_lr:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_my_lr:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse_my_lr:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_my_lr:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_my_lr:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_my_lr:.2f} 秒\")"
   ],
   "id": "46234a7c32058747",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.7112\n",
      "精确率(Precision): 0.7586\n",
      "均方误差(MSE): 0.2888\n",
      "召回率(Recall): 0.6196\n",
      "F1分数(F1 Score): 0.6821\n",
      "训练时间(Training Time): 127.78 秒\n"
     ]
    }
   ],
   "execution_count": 16
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T09:25:37.930581Z",
     "start_time": "2025-06-12T08:58:53.764669Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 手写线性SVM分类器\n",
    "class MySVM:\n",
    "    def __init__(self, learning_rate=0.001, max_iter=1000, C=1.0):\n",
    "        self.learning_rate = learning_rate\n",
    "        self.max_iter = max_iter\n",
    "        self.C = C  # 正则化参数\n",
    "        self.weights = None\n",
    "        self.bias = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        # 确保X和y是NumPy数组\n",
    "        X = np.array(X)\n",
    "        y = np.array(y)\n",
    "\n",
    "        # 转换标签为{-1, 1}\n",
    "        y_ = np.where(y <= 0, -1, 1)\n",
    "\n",
    "        n_samples, n_features = X.shape\n",
    "        self.weights = np.zeros(n_features)\n",
    "        self.bias = 0\n",
    "\n",
    "        # 梯度下降\n",
    "        for _ in range(self.max_iter):\n",
    "            for idx, x_i in enumerate(X):\n",
    "                condition = y_[idx] * (np.dot(x_i, self.weights) - self.bias) >= 1\n",
    "                if condition:\n",
    "                    self.weights -= self.learning_rate * (2 * self.C * self.weights)\n",
    "                else:\n",
    "                    self.weights -= self.learning_rate * (2 * self.C * self.weights - np.dot(x_i, y_[idx]))\n",
    "                    self.bias -= self.learning_rate * y_[idx]\n",
    "\n",
    "    def predict(self, X):\n",
    "        X = np.array(X)  # 确保X是NumPy数组\n",
    "        predictions = np.dot(X, self.weights) - self.bias\n",
    "        return np.sign(predictions)\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews).toarray()  # 转换为numpy数组\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews).toarray()\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练手写的SVM模型并计时\n",
    "start_time = time.time()\n",
    "my_svm = MySVM(learning_rate=0.001, max_iter=1000, C=1.0)\n",
    "my_svm.fit(X_train, y_train)\n",
    "train_time_my_svm = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = my_svm.predict(X_test)\n",
    "\n",
    "# 将预测结果从{-1, 1}转换回{0, 1}（如果原始标签是{0, 1}）\n",
    "y_pred = np.where(y_pred == -1, 0, 1)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_my_svm = accuracy_score(y_test, y_pred)\n",
    "precision_my_svm = precision_score(y_test, y_pred)\n",
    "mse_my_svm = mean_squared_error(y_test, y_pred)\n",
    "recall_my_svm = recall_score(y_test, y_pred)\n",
    "f1_my_svm = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy_my_svm:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_my_svm:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse_my_svm:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_my_svm:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_my_svm:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_my_svm:.2f} 秒\")"
   ],
   "id": "43597bffc907ffa5",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.5960\n",
      "精确率(Precision): 0.8458\n",
      "均方误差(MSE): 0.4040\n",
      "召回率(Recall): 0.2348\n",
      "F1分数(F1 Score): 0.3676\n",
      "训练时间(Training Time): 1516.52 秒\n"
     ]
    }
   ],
   "execution_count": 20
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T09:27:40.272196Z",
     "start_time": "2025-06-12T09:26:30.178516Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 计算信息熵\n",
    "def entropy(y):\n",
    "    unique, counts = np.unique(y, return_counts=True)\n",
    "    probabilities = counts / counts.sum()\n",
    "    return -np.sum(probabilities * np.log2(probabilities))\n",
    "\n",
    "# 计算信息增益\n",
    "def information_gain(X_column, X_threshold, y):\n",
    "    parent_entropy = entropy(y)\n",
    "    left_indices = X_column < X_threshold\n",
    "    right_indices = X_column >= X_threshold\n",
    "    left_entropy = entropy(y[left_indices])\n",
    "    right_entropy = entropy(y[right_indices])\n",
    "    n = len(y)\n",
    "    n_left = np.sum(left_indices)\n",
    "    n_right = np.sum(right_indices)\n",
    "    child_entropy = (n_left / n) * left_entropy + (n_right / n) * right_entropy\n",
    "    ig = parent_entropy - child_entropy\n",
    "    return ig\n",
    "\n",
    "# 决策树节点\n",
    "class DecisionTreeNode:\n",
    "    def __init__(self, feature=None, threshold=None, left=None, right=None, value=None):\n",
    "        self.feature = feature\n",
    "        self.threshold = threshold\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "        self.value = value\n",
    "\n",
    "    def is_leaf_node(self):\n",
    "        return self.value is not None\n",
    "\n",
    "# 手写决策树分类器\n",
    "class MyDecisionTreeClassifier:\n",
    "    def __init__(self, max_depth=None):\n",
    "        self.max_depth = max_depth\n",
    "        self.tree = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.tree = self._grow_tree(X, y)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return np.array([self._traverse_tree(x, self.tree) for x in X])\n",
    "\n",
    "    def _grow_tree(self, X, y, depth=0):\n",
    "        n_samples, n_features = X.shape\n",
    "        n_labels = len(np.unique(y))\n",
    "\n",
    "        # 停止条件\n",
    "        if depth == self.max_depth or n_labels == 1:\n",
    "            leaf_value = self._most_common_label(y)\n",
    "            return DecisionTreeNode(value=leaf_value)\n",
    "\n",
    "        # 寻找最佳分裂点\n",
    "        best_feature, best_threshold = self._best_split(X, y)\n",
    "        left_indices, right_indices = self._split(X[:, best_feature], best_threshold)\n",
    "        left_child = self._grow_tree(X[left_indices, :], y[left_indices], depth + 1)\n",
    "        right_child = self._grow_tree(X[right_indices, :], y[right_indices], depth + 1)\n",
    "\n",
    "        return DecisionTreeNode(feature=best_feature, threshold=best_threshold, left=left_child, right=right_child)\n",
    "\n",
    "    def _best_split(self, X, y):\n",
    "        best_gain = -1\n",
    "        split_index, split_threshold = None, None\n",
    "        n_features = X.shape[1]\n",
    "\n",
    "        for feature_index in range(n_features):\n",
    "            X_column = X[:, feature_index]\n",
    "            thresholds = np.unique(X_column)\n",
    "            for threshold in thresholds:\n",
    "                gain = information_gain(X_column, threshold, y)\n",
    "                if gain > best_gain:\n",
    "                    best_gain = gain\n",
    "                    split_index = feature_index\n",
    "                    split_threshold = threshold\n",
    "\n",
    "        return split_index, split_threshold\n",
    "\n",
    "    def _split(self, X_column, split_threshold):\n",
    "        left_indices = np.argwhere(X_column < split_threshold).flatten()\n",
    "        right_indices = np.argwhere(X_column >= split_threshold).flatten()\n",
    "        return left_indices, right_indices\n",
    "\n",
    "    def _most_common_label(self, y):\n",
    "        return np.argmax(np.bincount(y))\n",
    "\n",
    "    def _traverse_tree(self, x, node):\n",
    "        if node.is_leaf_node():\n",
    "            return node.value\n",
    "        if x[node.feature] < node.threshold:\n",
    "            return self._traverse_tree(x, node.left)\n",
    "        return self._traverse_tree(x, node.right)\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews).toarray()  # 转换为numpy数组\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews).toarray()\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练手写的决策树模型并计时\n",
    "start_time = time.time()\n",
    "my_dt = MyDecisionTreeClassifier(max_depth=10)\n",
    "my_dt.fit(X_train, y_train)\n",
    "train_time_my_dt = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = my_dt.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_my_dt = accuracy_score(y_test, y_pred)\n",
    "precision_my_dt = precision_score(y_test, y_pred)\n",
    "recall_my_dt = recall_score(y_test, y_pred)\n",
    "f1_my_dt = f1_score(y_test, y_pred)\n",
    "\n",
    "# 确保训练时间不会因为太小而报错\n",
    "train_time_my_dt = max(train_time_my_dt, 0.001)  # 设置最小值为0.001秒\n",
    "\n",
    "# 打印结果\n",
    "print(f\"准确率(Accuracy): {accuracy_my_dt:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_my_dt:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_my_dt:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_my_dt:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_my_dt:.2f} 秒\")"
   ],
   "id": "92226b62f853010f",
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "only integer scalar arrays can be converted to a scalar index",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mTypeError\u001B[0m                                 Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[21], line 107\u001B[0m\n\u001B[0;32m    105\u001B[0m start_time \u001B[38;5;241m=\u001B[39m time\u001B[38;5;241m.\u001B[39mtime()\n\u001B[0;32m    106\u001B[0m my_dt \u001B[38;5;241m=\u001B[39m MyDecisionTreeClassifier(max_depth\u001B[38;5;241m=\u001B[39m\u001B[38;5;241m10\u001B[39m)\n\u001B[1;32m--> 107\u001B[0m my_dt\u001B[38;5;241m.\u001B[39mfit(X_train, y_train)\n\u001B[0;32m    108\u001B[0m train_time_my_dt \u001B[38;5;241m=\u001B[39m time\u001B[38;5;241m.\u001B[39mtime() \u001B[38;5;241m-\u001B[39m start_time\n\u001B[0;32m    110\u001B[0m \u001B[38;5;66;03m# 预测\u001B[39;00m\n",
      "Cell \u001B[1;32mIn[21], line 44\u001B[0m, in \u001B[0;36mMyDecisionTreeClassifier.fit\u001B[1;34m(self, X, y)\u001B[0m\n\u001B[0;32m     43\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mfit\u001B[39m(\u001B[38;5;28mself\u001B[39m, X, y):\n\u001B[1;32m---> 44\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mtree \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_grow_tree(X, y)\n",
      "Cell \u001B[1;32mIn[21], line 59\u001B[0m, in \u001B[0;36mMyDecisionTreeClassifier._grow_tree\u001B[1;34m(self, X, y, depth)\u001B[0m\n\u001B[0;32m     56\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m DecisionTreeNode(value\u001B[38;5;241m=\u001B[39mleaf_value)\n\u001B[0;32m     58\u001B[0m \u001B[38;5;66;03m# 寻找最佳分裂点\u001B[39;00m\n\u001B[1;32m---> 59\u001B[0m best_feature, best_threshold \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_best_split(X, y)\n\u001B[0;32m     60\u001B[0m left_indices, right_indices \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_split(X[:, best_feature], best_threshold)\n\u001B[0;32m     61\u001B[0m left_child \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_grow_tree(X[left_indices, :], y[left_indices], depth \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m)\n",
      "Cell \u001B[1;32mIn[21], line 75\u001B[0m, in \u001B[0;36mMyDecisionTreeClassifier._best_split\u001B[1;34m(self, X, y)\u001B[0m\n\u001B[0;32m     73\u001B[0m thresholds \u001B[38;5;241m=\u001B[39m np\u001B[38;5;241m.\u001B[39munique(X_column)\n\u001B[0;32m     74\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m threshold \u001B[38;5;129;01min\u001B[39;00m thresholds:\n\u001B[1;32m---> 75\u001B[0m     gain \u001B[38;5;241m=\u001B[39m information_gain(X_column, threshold, y)\n\u001B[0;32m     76\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m gain \u001B[38;5;241m>\u001B[39m best_gain:\n\u001B[0;32m     77\u001B[0m         best_gain \u001B[38;5;241m=\u001B[39m gain\n",
      "Cell \u001B[1;32mIn[21], line 16\u001B[0m, in \u001B[0;36minformation_gain\u001B[1;34m(X_column, X_threshold, y)\u001B[0m\n\u001B[0;32m     14\u001B[0m left_indices \u001B[38;5;241m=\u001B[39m X_column \u001B[38;5;241m<\u001B[39m X_threshold\n\u001B[0;32m     15\u001B[0m right_indices \u001B[38;5;241m=\u001B[39m X_column \u001B[38;5;241m>\u001B[39m\u001B[38;5;241m=\u001B[39m X_threshold\n\u001B[1;32m---> 16\u001B[0m left_entropy \u001B[38;5;241m=\u001B[39m entropy(y[left_indices])\n\u001B[0;32m     17\u001B[0m right_entropy \u001B[38;5;241m=\u001B[39m entropy(y[right_indices])\n\u001B[0;32m     18\u001B[0m n \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mlen\u001B[39m(y)\n",
      "\u001B[1;31mTypeError\u001B[0m: only integer scalar arrays can be converted to a scalar index"
     ]
    }
   ],
   "execution_count": 21
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.261093400Z",
     "start_time": "2025-06-10T01:32:25.068634Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 计算信息熵\n",
    "def entropy(y):\n",
    "    unique, counts = np.unique(y, return_counts=True)\n",
    "    probabilities = counts / counts.sum()\n",
    "    return -np.sum(probabilities * np.log2(probabilities))\n",
    "\n",
    "# 计算信息增益\n",
    "def information_gain(X_column, X_threshold, y):\n",
    "    parent_entropy = entropy(y)\n",
    "    left_indices = X_column < X_threshold\n",
    "    right_indices = X_column >= X_threshold\n",
    "    left_entropy = entropy(y[left_indices])\n",
    "    right_entropy = entropy(y[right_indices])\n",
    "    n = len(y)\n",
    "    n_left = np.sum(left_indices)\n",
    "    n_right = np.sum(right_indices)\n",
    "    child_entropy = (n_left / n) * left_entropy + (n_right / n) * right_entropy\n",
    "    ig = parent_entropy - child_entropy\n",
    "    return ig\n",
    "\n",
    "# 决策树节点\n",
    "class DecisionTreeNode:\n",
    "    def __init__(self, feature=None, threshold=None, left=None, right=None, value=None):\n",
    "        self.feature = feature\n",
    "        self.threshold = threshold\n",
    "        self.left = left\n",
    "        self.right = right\n",
    "        self.value = value\n",
    "\n",
    "    def is_leaf_node(self):\n",
    "        return self.value is not None\n",
    "\n",
    "# 手写决策树分类器\n",
    "class MyDecisionTreeClassifier:\n",
    "    def __init__(self, max_depth=None):\n",
    "        self.max_depth = max_depth\n",
    "        self.tree = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.tree = self._grow_tree(X, y)\n",
    "\n",
    "    def predict(self, X):\n",
    "        return np.array([self._traverse_tree(x, self.tree) for x in X])\n",
    "\n",
    "    def _grow_tree(self, X, y, depth=0):\n",
    "        n_samples, n_features = X.shape\n",
    "        n_labels = len(np.unique(y))\n",
    "\n",
    "        # 停止条件\n",
    "        if depth == self.max_depth or n_labels == 1:\n",
    "            leaf_value = self._most_common_label(y)\n",
    "            return DecisionTreeNode(value=leaf_value)\n",
    "\n",
    "        # 寻找最佳分裂点\n",
    "        best_feature, best_threshold = self._best_split(X, y)\n",
    "        left_indices, right_indices = self._split(X[:, best_feature], best_threshold)\n",
    "        left_child = self._grow_tree(X[left_indices, :], y[left_indices], depth + 1)\n",
    "        right_child = self._grow_tree(X[right_indices, :], y[right_indices], depth + 1)\n",
    "\n",
    "        return DecisionTreeNode(feature=best_feature, threshold=best_threshold, left=left_child, right=right_child)\n",
    "\n",
    "    def _best_split(self, X, y):\n",
    "        best_gain = -1\n",
    "        split_index, split_threshold = None, None\n",
    "        n_features = X.shape[1]\n",
    "\n",
    "        for feature_index in range(n_features):\n",
    "            X_column = X[:, feature_index]\n",
    "            thresholds = np.unique(X_column)\n",
    "            for threshold in thresholds:\n",
    "                gain = information_gain(X_column, threshold, y)\n",
    "                if gain > best_gain:\n",
    "                    best_gain = gain\n",
    "                    split_index = feature_index\n",
    "                    split_threshold = threshold\n",
    "\n",
    "        return split_index, split_threshold\n",
    "\n",
    "    def _split(self, X_column, split_threshold):\n",
    "        left_indices = np.argwhere(X_column < split_threshold).flatten()\n",
    "        right_indices = np.argwhere(X_column >= split_threshold).flatten()\n",
    "        return left_indices, right_indices\n",
    "\n",
    "    def _most_common_label(self, y):\n",
    "        return np.argmax(np.bincount(y))\n",
    "\n",
    "    def _traverse_tree(self, x, node):\n",
    "        if node.is_leaf_node():\n",
    "            return node.value\n",
    "        if x[node.feature] < node.threshold:\n",
    "            return self._traverse_tree(x, node.left)\n",
    "        return self._traverse_tree(x, node.right)\n",
    "\n",
    "# 手写随机森林分类器\n",
    "class MyRandomForestClassifier:\n",
    "    def __init__(self, n_estimators=10, max_depth=None, max_features=None):\n",
    "        self.n_estimators = n_estimators\n",
    "        self.max_depth = max_depth\n",
    "        self.max_features = max_features\n",
    "        self.trees = []\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        n_samples, n_features = X.shape\n",
    "        if self.max_features is None:\n",
    "            self.max_features = int(np.sqrt(n_features))\n",
    "\n",
    "        for _ in range(self.n_estimators):\n",
    "            # 随机选择样本（有放回）\n",
    "            indices = np.random.choice(n_samples, n_samples, replace=True)\n",
    "            X_sample, y_sample = X[indices], y[indices]\n",
    "\n",
    "            # 随机选择特征子集\n",
    "            feature_indices = np.random.choice(n_features, self.max_features, replace=False)\n",
    "            X_sample = X_sample[:, feature_indices]\n",
    "\n",
    "            # 训练决策树\n",
    "            tree = MyDecisionTreeClassifier(max_depth=self.max_depth)\n",
    "            tree.fit(X_sample, y_sample)\n",
    "            self.trees.append((tree, feature_indices))\n",
    "\n",
    "    def predict(self, X):\n",
    "        tree_predictions = np.array([tree.predict(X[:, feature_indices]) for tree, feature_indices in self.trees])\n",
    "        majority_vote = np.apply_along_axis(lambda x: np.bincount(x).argmax(), axis=0, arr=tree_predictions)\n",
    "        return majority_vote\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews).toarray()  # 转换为numpy数组\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews).toarray()\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练手写的随机森林模型并计时\n",
    "start_time = time.time()\n",
    "my_rf = MyRandomForestClassifier(n_estimators=10, max_depth=10)\n",
    "my_rf.fit(X_train, y_train)\n",
    "train_time_my_rf = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = my_rf.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_my_rf = accuracy_score(y_test, y_pred)\n",
    "precision_my_rf = precision_score(y_test, y_pred)\n",
    "mse_my_rf = mean_squared_error(y_test, y_pred)\n",
    "recall_my_rf = recall_score(y_test, y_pred)\n",
    "f1_my_rf = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy_my_rf:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_my_rf:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse_my_rf:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_my_rf:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_my_rf:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_my_rf:.2f} 秒\")"
   ],
   "id": "e48e83eb8b3a62a6",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.8442\n",
      "精确率(Precision): 0.8505\n",
      "均方误差(MSE): 0.1558\n",
      "召回率(Recall): 0.8352\n",
      "F1分数(F1 Score): 0.8428\n",
      "训练时间(Training Time): 46.26 秒\n"
     ]
    }
   ],
   "execution_count": 31
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T10:38:37.386083Z",
     "start_time": "2025-06-12T09:28:12.095905Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_score, mean_squared_error, recall_score, f1_score\n",
    "import time\n",
    "\n",
    "# 计算欧氏距离\n",
    "def euclidean_distance(x1, x2):\n",
    "    return np.sqrt(np.sum((x1 - x2) ** 2))\n",
    "\n",
    "# 手写KNN分类器\n",
    "class MyKNNClassifier:\n",
    "    def __init__(self, k=3):\n",
    "        self.k = k\n",
    "        self.X_train = None\n",
    "        self.y_train = None\n",
    "\n",
    "    def fit(self, X, y):\n",
    "        self.X_train = X\n",
    "        self.y_train = y\n",
    "\n",
    "    def predict(self, X):\n",
    "        y_pred = [self._predict(x) for x in X]\n",
    "        return np.array(y_pred)\n",
    "\n",
    "    def _predict(self, x):\n",
    "        # 计算与所有训练样本的距离\n",
    "        distances = [euclidean_distance(x, x_train) for x_train in self.X_train]\n",
    "        # 获取距离最小的k个邻居的索引\n",
    "        k_indices = np.argsort(distances)[:self.k]\n",
    "        # 获取这些邻居的标签\n",
    "        k_nearest_labels = [self.y_train[i] for i in k_indices]\n",
    "        # 进行投票，选择出现次数最多的标签\n",
    "        most_common = np.argmax(np.bincount(k_nearest_labels))\n",
    "        return most_common\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews).toarray()  # 转换为numpy数组\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews).toarray()\n",
    "y_train = train_labels\n",
    "y_test = test_labels\n",
    "\n",
    "# 训练手写的KNN模型并计时\n",
    "start_time = time.time()\n",
    "my_knn = MyKNNClassifier(k=5)\n",
    "my_knn.fit(X_train, y_train)\n",
    "train_time_my_knn = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = my_knn.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_my_knn = accuracy_score(y_test, y_pred)\n",
    "precision_my_knn = precision_score(y_test, y_pred)\n",
    "mse_my_knn = mean_squared_error(y_test, y_pred)\n",
    "recall_my_knn = recall_score(y_test, y_pred)\n",
    "f1_my_knn = f1_score(y_test, y_pred)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy_my_knn:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_my_knn:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse_my_knn:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_my_knn:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_my_knn:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_my_knn:.2f} 秒\")"
   ],
   "id": "fe1e0a0f32a54ea7",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.7048\n",
      "精确率(Precision): 0.7116\n",
      "均方误差(MSE): 0.2952\n",
      "召回率(Recall): 0.6888\n",
      "F1分数(F1 Score): 0.7000\n",
      "训练时间(Training Time): 0.00 秒\n"
     ]
    }
   ],
   "execution_count": 22
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T11:00:42.822593Z",
     "start_time": "2025-06-12T10:59:49.485260Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import time\n",
    "import numpy as np\n",
    "\n",
    "class NaiveBayesClassifier:\n",
    "    def fit(self, X, y):\n",
    "        self.classes = np.unique(y)\n",
    "        self.class_priors = {}\n",
    "        self.feature_probs = {}\n",
    "        X = X.toarray()\n",
    "        for c in self.classes:\n",
    "            X_c = X[y == c]\n",
    "            self.class_priors[c] = X_c.shape[0] / X.shape[0]\n",
    "            # 拉普拉斯平滑\n",
    "            self.feature_probs[c] = (X_c.sum(axis=0) + 1) / (X_c.sum() + X.shape[1])\n",
    "    def predict(self, X):\n",
    "        X = X.toarray()\n",
    "        preds = []\n",
    "        for x in X:\n",
    "            class_scores = {}\n",
    "            for c in self.classes:\n",
    "                # 计算对数概率防止下溢\n",
    "                log_prob = np.log(self.class_priors[c])\n",
    "                log_prob += (x * np.log(self.feature_probs[c])).sum()\n",
    "                class_scores[c] = log_prob\n",
    "            preds.append(max(class_scores, key=class_scores.get))\n",
    "        return np.array(preds, dtype=int)\n",
    "\n",
    "# 使用TF-IDF特征\n",
    "X_train = tfidf_vectorizer_imdb.transform(train_reviews)\n",
    "X_test = tfidf_vectorizer_imdb.transform(test_reviews)\n",
    "y_train = np.array(train_labels)\n",
    "y_test = np.array(test_labels)\n",
    "\n",
    "# 训练朴素贝叶斯模型并计时\n",
    "start_time = time.time()\n",
    "nb = NaiveBayesClassifier()\n",
    "nb.fit(X_train, y_train)\n",
    "train_time_nb = time.time() - start_time\n",
    "\n",
    "# 预测\n",
    "y_pred = nb.predict(X_test)\n",
    "\n",
    "# 评估指标\n",
    "accuracy_nb = np.mean(y_pred == y_test)\n",
    "tp = np.sum((y_pred == 1) & (y_test == 1))\n",
    "fp = np.sum((y_pred == 1) & (y_test == 0))\n",
    "fn = np.sum((y_pred == 0) & (y_test == 1))\n",
    "precision_nb = tp / (tp + fp) if (tp + fp) > 0 else 0\n",
    "recall_nb = tp / (tp + fn) if (tp + fn) > 0 else 0\n",
    "f1_nb = 2 * precision_nb * recall_nb / (precision_nb + recall_nb) if (precision_nb + recall_nb) > 0 else 0\n",
    "mse_nb = np.mean((y_pred - y_test) ** 2)\n",
    "\n",
    "print(f\"准确率(Accuracy): {accuracy_nb:.4f}\")\n",
    "print(f\"精确率(Precision): {precision_nb:.4f}\")\n",
    "print(f\"均方误差(MSE): {mse_nb:.4f}\")\n",
    "print(f\"召回率(Recall): {recall_nb:.4f}\")\n",
    "print(f\"F1分数(F1 Score): {f1_nb:.4f}\")\n",
    "print(f\"训练时间(Training Time): {train_time_nb:.2f} 秒\")"
   ],
   "id": "5362f0a89d92a60c",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "准确率(Accuracy): 0.8712\n",
      "精确率(Precision): 0.8763\n",
      "均方误差(MSE): 0.1288\n",
      "召回率(Recall): 0.8644\n",
      "F1分数(F1 Score): 0.8703\n",
      "训练时间(Training Time): 1.08 秒\n"
     ]
    }
   ],
   "execution_count": 23
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.263234700Z",
     "start_time": "2025-06-10T01:35:05.573650Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "5321e330e79eea6b",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\任老大\\AppData\\Local\\Temp\\ipykernel_14892\\876259127.py:26: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[0].set_xticklabels(model_names, rotation=30)\n",
      "C:\\Users\\任老大\\AppData\\Local\\Temp\\ipykernel_14892\\876259127.py:30: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[1].set_xticklabels(model_names, rotation=30)\n",
      "C:\\Users\\任老大\\AppData\\Local\\Temp\\ipykernel_14892\\876259127.py:34: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[2].set_xticklabels(model_names, rotation=30)\n",
      "C:\\Users\\任老大\\AppData\\Local\\Temp\\ipykernel_14892\\876259127.py:38: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[3].set_xticklabels(model_names, rotation=30)\n",
      "C:\\Users\\任老大\\AppData\\Local\\Temp\\ipykernel_14892\\876259127.py:42: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
      "  axes[4].set_xticklabels(model_names, rotation=30)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 4000x1000 with 5 Axes>"
      ],
      "image/png": "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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.263234700Z",
     "start_time": "2025-06-10T01:35:06.592395Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "1c627293d6402968",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T08:17:13.264556300Z",
     "start_time": "2025-06-10T01:35:06.779280Z"
    }
   },
   "cell_type": "code",
   "source": "",
   "id": "535624eef77084ec",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
